Computer Methods and Devices for Detecting Kidney Damage

ABSTRACT

Methods and devices for diagnosing, monitoring, or determining a renal disorder in a mammal are described. In particular, methods and devices for diagnosing, monitoring, or determining a renal disorder using measured concentrations of a combination of three or more analytes in a test sample taken from the mammal are described.

RELATED APPLICATIONS

This application takes priority to U.S. Provisional Patent ApplicationNo. 61/327,389, filed Apr. 23, 2010 and U.S. Provisional PatentApplication No. 61/232,091, filed Aug. 7, 2009, and both entitledMethods and Devices for Detecting Kidney Damage, the entire contents ofwhich are incorporated herein by reference, and is related to U.S.Patent Application Nos. [Not Yet Assigned], entitled Methods and Devicesfor Detecting Obstructive Uropathy and Associated Disorders, Methods andDevices for Detecting Kidney Damage, Devices for Detecting RenalDisorders, Methods and Devices for Detecting Kidney TransplantRejection, Methods and Devices for Detecting Diabetic Nephropathy andAssociated Disorders, and Methods and Devices for DetectingGlomerulonephritis and Associated Disorders, Attorney Docket Nos.060075-, filed on the same date as this application, the entire contentsof which are incorporated herein by reference.

FIELD OF THE INVENTION

The invention encompasses methods and devices for diagnosing,monitoring, or determining a renal disorder in a mammal. In particular,the present invention provides methods and devices for diagnosing,monitoring, or determining a renal disorder using measuredconcentrations of a combination of three or more analytes in a testsample taken from the mammal.

BACKGROUND OF THE INVENTION

The urinary system, in particular the kidneys, perform several criticalfunctions such as maintaining electrolyte balance and eliminating toxinsfrom the bloodstream. In the human body, the pair of kidneys togetherprocess roughly 20% of the total cardiac output, amounting to about 1L/min in a 70-kg adult male. Because compounds in circulation areconcentrated in the kidney up to 1000-fold relative to the plasmaconcentration, the kidney is especially vulnerable to injury due toexposure to toxic compounds.

In the pharmaceutical industry, drug-induced kidney injury is a majorcause for delay during the development of candidate drugs. Historically,regulatory agencies have required drug companies to provide results ofblood urea nitrogen (BUN) and serum creatinine tests, two commondiagnostic tests for renal function, to address concerns of potentialkidney damage as part of the regulatory approval process. However, thesediagnostic tests typically detect only late signs of kidney damage andprovide little information as to the location of kidney damage.

In addition to injuries resulting from exposure to drugs or other toxiccompounds, kidney damage may also result from renal disorders such askidney trauma, nephritis, kidney cancer, and kidney transplantrejection. Kidney damage may also occur as a secondary side effect ofmore systemic diseases such as diabetes, hypertension, and autoimmunediseases. Existing diagnostic tests such as BUN and serum creatine teststypically detect only advanced stages of kidney damage. Other diagnostictests such as kidney tissue biopsies or CAT scans have the advantage ofenhanced sensitivity to earlier stages of kidney damage, but these testsare also generally costly, slow, and/or invasive.

A need exists in the art for a fast, simple, reliable, and sensitivemethod of detecting a renal disorder. The detection of the early signsand locations of drug-induced kidney damage would be useful in guidingimportant decisions on lead compounds and dosage. In a clinical setting,the early detection of kidney damage would help medical practitioners todiagnose and treat kidney damage more quickly and effectively.

SUMMARY OF THE INVENTION

The present invention provides computer methods and devices fordiagnosing, monitoring, or determining a renal disorder in a mammal. Inparticular, the present invention provides methods and devices fordiagnosing, monitoring, or determining a renal disorder using measuredconcentrations of a combination of three or more analytes in a testsample taken from the mammal.

One aspect of the present invention provides a method for diagnosing,monitoring, or determining a renal disorder in a mammal that includesproviding a test sample that includes a sample of bodily fluid takenfrom the mammal, and determining the presence of a combination of threeor more sample analytes in the test sample. The analytes in the testsample may include but are not limited to alpha-1 microglobulin, beta-2microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C,GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3,and VEGF. The combination of sample analytes is compared to the entriesof a dataset in which each entry includes a combination of three or morediagnostic analytes reflective of a particular renal disorder. Theparticular renal disorder of the mammal is identified as the renaldisorder in the database having the combination of diagnostic analytesthat essentially match the combination of sample analytes.

In another aspect, a method for diagnosing, monitoring, or determining arenal disorder in a mammal is provided that includes providing a testsample that includes a sample of bodily fluid taken from the mammal anddetermining a combination of sample concentrations for three or moresample analytes in the test sample. The analytes may include but are notlimited to alpha-1 microglobulin, beta-2 microglobulin, calbindin,clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin,NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. The combination ofsample concentrations is compared to the entries of a dataset in whicheach entry includes a particular renal disorder and a list of three ormore minimum diagnostic concentrations indicative of the particularrenal disorder. Each minimum diagnostic concentration is the maximumconcentration of a range of analyte concentrations for a healthy mammal.A matching entry is determined in which all minimum diagnosticconcentrations are less than the corresponding sample concentrations,and an indicated renal disorder is identified as the particular renaldisorder of the matching entry.

In yet another aspect, a method for diagnosing, monitoring, ordetermining a renal disorder in a mammal is provided that includesproviding a test sample that includes a sample of bodily fluid takenfrom the mammal and determining a combination of sample concentrationsconsisting of the concentrations of calbindin, clusterin, CTGF,GST-alpha, KIM-1, and VEGF in the test sample. The combination of sampleconcentrations is compared to the entries of a data set in which eachentry includes a particular renal disorder and a list of three or moreminimum diagnostic concentrations indicative of the particular renaldisorder. A matching entry is determined in which all minimum diagnosticconcentrations are less than the corresponding sample concentrations,and an indicated renal disorder is identified as the particular renaldisorder of the matching entry.

In still another aspect, a method for diagnosing, monitoring, ordetermining a renal disorder in a mammal is provided that includesproviding a test sample that includes a sample of bodily fluid takenfrom the mammal and determining a combination of sample concentrationsconsisting of the concentrations of beta-2 microglobulin, cystatin C,NGAL, osteopontin, and TIMP-1 in the test sample. The combination ofsample concentrations is compared to the entries of a data set in whicheach entry includes a particular renal disorder and a list of three ormore minimum diagnostic concentrations indicative of the particularrenal disorder. A matching entry is determined in which all minimumdiagnostic concentrations are less than the corresponding sampleconcentrations, and an indicated renal disorder is identified as theparticular renal disorder of the matching entry.

In an additional aspect, a method for diagnosing, monitoring, ordetermining a renal disorder in a mammal is provided that includesproviding a test sample that includes a sample of bodily fluid takenfrom the mammal and determining a combination of sample concentrationsconsisting of the concentrations of alpha-1 microglobulin, THP, andTFF-3 in the test sample. The combination of sample concentrations iscompared to the entries of a data set in which each entry includes aparticular renal disorder and a list of three or more minimum diagnosticconcentrations indicative of the particular renal disorder. A matchingentry is determined in which all minimum diagnostic concentrations areless than the corresponding sample concentrations, and an indicatedrenal disorder is identified as the particular renal disorder of thematching entry.

In yet another aspect, a method for diagnosing, monitoring, ordetermining a renal disorder in a mammal is provided. The methodincludes providing a test sample comprising a sample of bodily fluidtaken from the mammal and determining the concentrations of three ormore sample analytes in a panel of biomarkers in the test sample. Thesample analytes may be selected from the group consisting of alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF. Diagnostic analytes are thenidentified in the test sample, wherein the diagnostic analytes are thesample analytes whose concentrations are statistically different fromconcentrations found in a control group of humans who do not suffer froma renal disorder. The combination of diagnostic analytes are compared toa dataset comprising at least one entry, wherein each entry of thedataset comprises a combination of three or more diagnostic analytesreflective of a particular renal disorder. The particular renal disorderin the list is identified as the renal disorder having the combinationof diagnostic analytes that essentially match the combination of sampleanalytes.

An additional aspect provides a computer readable media encoded with anapplication that includes modules executable by a processor andconfigured to diagnose, monitor, or determine a renal disorder in amammal. An analyte input module receives three or more sample analyteconcentrations that may include alpha-1 microglobulin, beta-2microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C,GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3,and VEGF. A comparison module compares each sample analyte concentrationto an entry of a renal disorder database, where each entry includes alist of minimum diagnostic concentrations reflective of a particularrenal disorder. An analysis module determines a most likely renaldisorder by combining the particular renal disorders identified by thecomparison module for all of the sample analyte concentrations.

Yet another aspect provides a system for diagnosing, monitoring, ordetermining a renal disorder in a mammal that includes a database tostore a plurality of renal disorder database entries as well as aprocessing device that includes a renal disorder diagnosis applicationcontaining modules executable by the processing device. The modules ofthe renal disorder diagnosis application include an analyte input moduleto receive three or more sample analyte concentrations selected from thegroup consisting of alpha-1 microglobulin, beta-2 microglobulin,calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1,microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF. Anothermodule, the comparison module, compares each sample analyteconcentration to an entry of the renal disorder database. Each entry ofthe renal disorder database contains a list of minimum diagnosticconcentrations reflective of a particular renal disorder. An analysismodule determines a most likely renal disorder by combining theparticular renal disorders identified by the comparison module for allof the sample analyte concentrations.

An aspect provides a device for diagnosing, monitoring, or determining arenal disorder in a mammal that includes three or more antibodies and aplurality of indicators attached to each of the antibodies. Theantigenic determinants of the antibodies are analytes associated with arenal disorder including but not limited to alpha-1 microglobulin,beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatinC, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1,TFF-3, and VEGF.

Another aspect provides a device for diagnosing, monitoring, ordetermining a renal disorder in a mammal that includes three or morecapture antibodies, three or more capture agents, three or moredetection antibodies, and three or more indicators. The antigenicdeterminants of the capture antibodies are analytes associated with arenal disorder including but not limited to alpha-1 microglobulin,beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatinC, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1,TFF-3, and VEGF. One of the capture agents is attached to each of thecapture antibodies, and includes an antigenic moiety. The antigenicdeterminant of the detection antibodies is the antigenic moiety. Each ofthe indicators is attached to one of the detection antibodies.

A final aspect provides a method for diagnosing, monitoring, ordetermining a renal disorder in a mammal that includes providing ananalyte concentration measurement device that includes three or moredetection antibodies, in which each detection antibody includes anantibody coupled to an indicator. The antigenic determinants of theantibodies are sample analytes associated with a renal disorderincluding but not limited to alpha-1 microglobulin, beta-2microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C,GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3,and VEGF. A test sample that contains three or more sample analytes anda bodily fluid taken from the mammal is provided and contacted with thedetection antibodies. The detection antibodies are allowed to bind tothe sample analytes. The concentrations of the sample analytes aredetermined by detecting the indicators of the detection antibodies boundto the sample analytes in the test sample. The concentrations of eachsample analyte are compared to a corresponding minimum diagnosticconcentration reflective of a particular renal disorder.

Other aspects and iterations of the invention are described in moredetail below.

DESCRIPTION OF FIGURES

FIG. 1 depicts four graphs comparing (A) the concentrations of alpha-1microglobulin in the urine of normal controls, kidney cancer patients,and patients with other cancer types; (B) the concentrations of beta-2microglobulin in the urine of normal controls, kidney cancer patients,and patients with other cancer types; (C) the concentrations of NGAL inthe urine of normal controls, kidney cancer patients, and patients withother cancer types; and (D) the concentrations of THP in the urine ofnormal controls, kidney cancer patients, and patients with other cancertypes.

FIG. 2 shows the four different disease groups from which samples wereanalyzed, and a plot of two different estimations on eGFR outlining thedistribution within each group.

FIG. 3 is a number of scatter plots of results on selected proteins inurine and plasma. The various groups are indicated as follows—control:blue, AA: red, DN: green, GN: yellow, OU: orange. (A) A1M in plasma, (B)cystatin C in plasma, (C) B2M in urine, (D) cystatin C in urine.

FIG. 4 depicts the multivariate analysis of the disease groups and theirrespective matched controls using plasma results. Relative importanceshown using the random forest model.

FIG. 5 depicts three graphs showing the mean AUROC and its standarddeviation (A) for plasma samples, and mean error rates (B) and meanAUROC (C) from urine samples for each classification method used todistinguish disease samples vs. normal samples. Disease encompassesanalgesic abuse (AA), glomerulonephritis (GN), obstructive uropathy(OU), and diabetic nephropathy (DN). Normal=NL.

FIG. 6 depicts three graphs showing the average importance of analytesand clinical variables from 100 bootstrap runs measured by random forest(A and B) or boosting (C) to distinguish disease (AA+GN+ON+DN) samplesvs. normal samples from plasma (A) and urine (B and C).

FIG. 7 depicts three graphs showing the mean AUROC and its standarddeviation (A) for plasma samples, and mean error rates (B) and meanAUROC (C) from urine samples for each classification method used todistinguish analgesic abuse samples vs. normal samples. Abbreviations asin FIG. 4.

FIG. 8 depicts three graphs showing the average importance of analytesand clinical variables from 100 bootstrap runs measured by random forest(A and B) or boosting (C) to distinguish analgesic abuse samples vs.normal samples from plasma (A) and urine (B and C).

FIG. 9 depicts three graphs showing the mean AUROC and its standarddeviation (A) for plasma samples, and mean error rates (B) and meanAUROC (C) from urine samples for each classification method used todistinguish analgesic abuse samples vs. diabetic nephropathy samples.Abbreviations as in FIG. 4.

FIG. 10 depicts three graphs showing the average importance of analytesand clinical variables from 100 bootstrap runs measured by random forest(A and B) or boosting (C) to distinguish analgesic abuse samples vs.diabetic nephropathy samples from plasma (A) and urine (B and C).

FIG. 11 depicts three graphs showing the mean AUROC and its standarddeviation (A) for plasma samples, and mean error rates (B) and meanAUROC (C) from urine samples for each classification method used todistinguish glomerulonephritis samples vs. analgesic abuse samples.Abbreviations as in FIG. 4.

FIG. 12 depicts three graphs showing the average importance of analytesand clinical variables from 100 bootstrap runs measured by random forest(A and B) or boosting (C) to distinguish glomerulonephritis samples vs.analgesic abuse samples from plasma (A) and urine (B and C).

FIG. 13 depicts three graphs showing the mean AUROC and its standarddeviation (A) for plasma samples, and mean error rates (B) and meanAUROC (C) from urine samples for each classification method used todistinguish obstructive uropathy samples vs. analgesic abuse samples.Abbreviations as in FIG. 4.

FIG. 14 depicts three graphs showing the average importance of analytesand clinical variables from 100 bootstrap runs measured by random forest(A and B) or boosting (C) to distinguish obstructive uropathy samplesvs. analgesic abuse samples from plasma (A) and urine (B and C).

FIG. 15 is a block diagram of an emplary computing environment forimplementing a renal disorder diagnostic system.

FIG. 16 is a block diagram that depicts an exemplary renal disorderdiagnostic system.

FIG. 17 illustrates a method for diagnosing, monitoring, or determininga renal disorder in a mammal in accordance with an aspect of the renaldisorder diagnostic system.

DETAILED DESCRIPTION OF THE INVENTION

It has been discovered that a multiplexed panel of up to 16 biomarkersmay be used to detect early renal damage and pinpoint the location ofrenal damage within the kidney. The biomarkers included in themultiplexed panel are analytes known in the art that may be detected inthe urine, serum, plasma and other bodily fluids of mammals. As such,the analytes of the multiplexed panel may be readily extracted from themammal in a test sample of bodily fluid. The concentrations of theanalytes within the test sample may be measured using known analyticaltechniques such as a multiplexed antibody-based immunological assay. Thecombination of concentrations of the analytes in the test sample may becompared to empirically determined combinations of minimum diagnosticconcentrations and combinations of diagnostic concentration rangesassociated with healthy kidney function or one or more particular renaldisorders to determine whether a renal disorder is indicated in themammal. The potentially large number of combinations of diagnosticanalyte concentrations makes possible a wide range of diagnosticcriteria that may be used to identify a variety of renal disorders andpinpoint the location in the kidney of a renal injury, using a singlemultiplexed assay to evaluate a single test sample.

As used herein, the term “renal disorder” includes, but is not limitedto glomerulonephritis, interstitial nephritis, tubular damage,vasculitis, glomerulosclerosis, analgesic nephropathy, and acute tubularnecrosis. In another embodiment, the multiplexed analyte panelidentifies secondary kidney damaged caused by exposure to a toxiccompound including but not limited to therapeutic drugs, recreationaldrugs, contrast agents, medical imaging contrast agents, and toxins.Non-limiting examples of therapeutic drugs may include an analgesic(e.g. aspirin, acetaminophen, ibuprofen, naproxen sodium), an antibiotic(e.g. an aminoglycoside, a beta lactam (cephalosporins, penicillins,penems), rifampin, vancomycin, a sulfonamide, a fluoroquinolone, and atetracycline), or a chemotherapy agent (e.g. Cisplatin (Platinol®),Carboplatin (Paraplatin®), Cytarabine (Cytosar-U®), Gemtuzumabozogamicin (Mylotarg®), Gemcitabine (Gemzar®), Melphalan (Alkeran®),Ifosfamide (Ifex®), Methotrexate (Rheumatrex®), Interleukin-2(Proleukin®), Oxaliplatin (Eloxatin®), Streptozocin (Zanosar®),Pemetrexed (Alimta®), Plicamycin (Mithracin®), and Trimetrexate(Neutrexin®). In yet another embodiment, the kidney damage may be due tokidney stones, ischemia, liver transplantation, heart transplantation,lung transplantation, or hypovolemia. In still another embodiment, themultiplexed analyte panel identifies kidney damage caused by diseaseincluding but not limited to diabetes, hypertension, autoimmune diseasesincluding lupus, Wegener's granulomatosis, Goodpasture syndrome, primaryhyperoxaluria, kidney transplant rejection, sepsis, nephritis secondaryto any infection of the kidney, rhabdomyolysis, multiple myeloma, andprostate disease.

One embodiment of the present invention provides a method fordiagnosing, monitoring, or determining a renal disorder in a mammal thatincludes determining the presence or concentration of a combination ofthree or more sample analytes in a test sample containing the bodilyfluid of the mammal. The measured concentrations of the combination ofsample analytes is compared to the entries of a dataset in which eachentry contains the minimum diagnostic concentrations of a combination ofthree of more analytes reflective of a particular renal disorder. Otherembodiments provide computer-readable media encoded with applicationscontaining executable modules, systems that include databases andprocessing devices containing executable modules configured to diagnose,monitor, or determine a renal disorder in a mammal. Still otherembodiments provide antibody-based devices for diagnosing, monitoring,or determining a renal disorder in a mammal.

The analytes used as biomarkers in the multiplexed assay, methods ofdiagnosing, monitoring, or determining a renal disorder usingmeasurements of the analytes, systems and applications used to analyzethe multiplexed assay measurements, and antibody-based devices used tomeasure the analytes are described in detail below.

I. Analytes in Multiplexed Assay

One embodiment of the invention measures the concentrations of at leastthree, six, or preferably sixteen biomarker analytes within a testsample taken from a mammal and compares the measured analyteconcentrations to minimum diagnostic concentrations to diagnose,monitor, or determine kidney damage in a mammal. In this aspect, thebiomarker analytes are known in the art to occur in the urine, plasma,serum and other bodily fluids of mammals. The biomarker analytes areproteins that have known and documented associations with early kidneydamage in humans. As defined herein, the biomarker analytes may includebut are not limited to alpha-1 microglobulin, beta-2 microglobulin,calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1,microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, VEGF A, BLC,CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM,Tenascin C, VCAM1, GST-mu, EGF, and cortisol. A description of some ofthe biomarker analytes are given below.

(a) Alpha-1 Microglobulin (A1M)

Alpha-1 microglobulin (A1M, Swiss-Prot Accession Number P02760) is a 26kDa glycoprotein synthesized by the liver and reabsorbed in the proximaltubules. Elevated levels of A1M in human urine are indicative ofglomerulotubular dysfunction. A1M is a member of the lipocalin superfamily and is found in all tissues. Alpha-1-microglobulin exists inblood in both a free form and complexed with immunoglobulin A (IgA) andheme. Half of plasma A1M exists in a free form, and the remainder existsin complexes with other molecules including prothrombin, albumin,immunoglobulin A and heme. Nearly all of the free A1M in human urine isreabsorbed by the megalin receptor in proximal tubular cells, where itis then catabolized. Small amounts of A1M are excreted in the urine ofhealthy humans. Increased A1M concentrations in human urine may be anearly indicator of renal damage, primarily in the proximal tubule.

(b) Beta-2 Microglobulin (B2M)

Beta-2 microglobulin (B2M, Swiss-Prot Accession Number P61769) is aprotein found on the surfaces of all nucleated cells and is shed intothe blood, particularly by tumor cells and lymphocytes. Due to its smallsize, B2M passes through the glomerular membrane, but normally less than1% is excreted due to reabsorption of B2M in the proximal tubules of thekidney. Therefore, high plasma levels of B2M occur as a result of renalfailure, inflammation, and neoplasms, especially those associated withB-lymphocytes.

(c) Calbindin

Calbindin (Calbindin D-28K, Swiss-Prot Accession Number P05937) is aCa-binding protein belonging to the troponin C superfamily. It isexpressed in the kidney, pancreatic islets, and brain. Calbindin isfound predominantly in subpopulations of central and peripheral nervoussystem neurons, in certain epithelial cells involved in Ca2+ transportsuch as distal tubular cells and cortical collecting tubules of thekidney, and in enteric neuroendocrine cells.

(d) Clusterin

Clusterin (Swiss-Prot Accession Number P10909) is a highly conservedprotein that has been identified independently by many differentlaboratories and named SGP2, S35-S45, apolipoprotein J, SP-40, 40,ADHC-9, gp80, GPIII, and testosterone-repressed prostate message(TRPM-2). An increase in clusterin levels has been consistently detectedin apoptotic heart, brain, lung, liver, kidney, pancreas, and retinaltissue both in vivo and in vitro, establishing clusterin as a ubiquitousmarker of apoptotic cell loss. However, clusterin protein has also beenimplicated in physiological processes that do not involve apoptosis,including the control of complement-mediated cell lysis, transport ofbeta-amyloid precursor protein, shuttling of aberrant beta-amyloidacross the blood-brain barrier, lipid scavenging, membrane remodeling,cell aggregation, and protection from immune detection and tumornecrosis factor induced cell death.

(e) Connective Tissue Growth Factor (CTGF)

Connective tissue growth factor (CTGF, Swiss-Prot Accession NumberP29279) is a 349-amino acid cysteine-rich polypeptide belonging to theCCN family. In vitro studies have shown that CTGF is mainly involved inextracellular matrix synthesis and fibrosis. Up-regulation of CTGF mRNAand increased CTGF levels have been observed in various diseases,including diabetic nephropathy and cardiomyopathy, fibrotic skindisorders, systemic sclerosis, biliary atresia, liver fibrosis andidiopathic pulmonary fibrosis, and nondiabetic acute and progressiveglomerular and tubulointerstitial lesions of the kidney. A recentcross-sectional study found that urinary CTGF may act as a progressionpromoter in diabetic nephropathy.

(f) Creatinine

Creatinine is a metabolite of creatine phosphate in muscle tissue, andis typically produced at a relatively constant rate by the body.Creatinine is chiefly filtered out of the blood by the kidneys, though asmall amount is actively secreted by the kidneys into the urine.Creatinine levels in blood and urine may be used to estimate thecreatinine clearance, which is representative of the overall glomerularfiltration rate (GFR), a standard measure of renal function. Variationsin creatinine concentrations in the blood and urine, as well asvariations in the ratio of urea to creatinine concentration in theblood, are common diagnostic measurements used to assess renal function.

(g) Cystatin C (Cyst C)

Cystatin C (Cyst C, Swiss-Prot Accession Number P01034) is a 13 kDaprotein that is a potent inhibitor of the C1 family of cysteineproteases. It is the most abundant extracellular inhibitor of cysteineproteases in testis, epididymis, prostate, seminal vesicles and manyother tissues. Cystatin C, which is normally expressed in vascular wallsmooth muscle cells, is severely reduced in both atherosclerotic andaneurismal aortic lesions.

(h) Glutathione S-Transferase Alpha (GST-Alpha)

Glutathione S-transferase alpha (GST-alpha, Swiss-Prot Accession NumberP08263) belongs to a family of enzymes that utilize glutathione inreactions contributing to the transformation of a wide range ofcompounds, including carcinogens, therapeutic drugs, and products ofoxidative stress. These enzymes play a key role in the detoxification ofsuch substances.

(i) Kidney Injury Molecule-1 (KIM-1)

Kidney injury molecule-1 (KIM-1, Swiss-Prot Accession Number Q96D42) isan immunoglobulin superfamily cell-surface protein highly upregulated onthe surface of injured kidney epithelial cells. It is also known asTIM-1 (T-cell immunoglobulin mucin domain-1), as it is expressed at lowlevels by subpopulations of activated T-cells and hepatitis A viruscellular receptor-1 (HAVCR-1). KIM-1 is increased in expression morethan any other protein in the injured kidney and is localizedpredominantly to the apical membrane of the surviving proximalepithelial cells.

(j) Microalbumin

Albumin is the most abundant plasma protein in humans and other mammals.Albumin is essential for maintaining the osmotic pressure needed forproper distribution of body fluids between intravascular compartmentsand body tissues. Healthy, normal kidneys typically filter out albuminfrom the urine. The presence of albumin in the urine may indicate damageto the kidneys. Albumin in the urine may also occur in patients withlong-standing diabetes, especially type 1 diabetes. The amount ofalbumin eliminated in the urine has been used to differentially diagnosevarious renal disorders. For example, nephrotic syndrome usually resultsin the excretion of about 3.0 to 3.5 grams of albumin in human urineevery 24 hours. Microalbuminuria, in which less than 300 mg of albuminis eliminated in the urine every 24 hours, may indicate the early stagesof diabetic nephropathy.

(k) Neutrophil Gelatinase-Associated Lipocalin (NGAL)

Neutrophil gelatinase-associated lipocalin (NGAL, Swiss-Prot AccessionNumber P80188) forms a disulfide bond-linked heterodimer with MMP-9. Itmediates an innate immune response to bacterial infection bysequestrating iron. Lipocalins interact with many different moleculessuch as cell surface receptors and proteases, and play a role in avariety of processes such as the progression of cancer and allergicreactions.

(I) Osteopontin (OPN)

Osteopontin (OPN, Swiss-Prot Accession Number P10451) is a cytokineinvolved in enhancing production of interferon-gamma and IL-12, andinhibiting the production of IL-10. OPN is essential in the pathway thatleads to type I immunity. OPN appears to form an integral part of themineralized matrix. OPN is synthesized within the kidney and has beendetected in human urine at levels that may effectively inhibit calciumoxalate crystallization. Decreased concentrations of OPN have beendocumented in urine from patients with renal stone disease compared withnormal individuals.

(m) Tamm-Horsfall Protein (THP)

Tamm-Horsfall protein (THP, Swiss-Prot Accession Number P07911), alsoknown as uromodulin, is the most abundant protein present in the urineof healthy subjects and has been shown to decrease in individuals withkidney stones. THP is secreted by the thick ascending limb of the loopof Henley. THP is a monomeric glycoprotein of ˜85 kDa with ˜30%carbohydrate moiety that is heavily glycosylated. THP may act as aconstitutive inhibitor of calcium crystallization in renal fluids.

(n) Tissue Inhibitor of Metalloproteinase-1 (TIMP-1)

Tissue inhibitor of metalloproteinase-1 (TIMP-1, Swiss-Prot AccessionNumber P01033) is a major regulator of extracellular matrix synthesisand degradation. A certain balance of MMPs and TIMPs is essential fortumor growth and health. Fibrosis results from an imbalance offibrogenesis and fibrolysis, highlighting the importance of the role ofthe inhibition of matrix degradation role in renal disease.

(o) Trefoil Factor 3 (TFF3)

Trefoil factor 3 (TFF3, Swiss-Prot Accession Number Q07654), also knownas intestinal trefoil factor, belongs to a small family ofmucin-associated peptides that include TFF1, TFF2, and TFF3. TFF3 existsin a 60-amino acid monomeric form and a 118-amino acid dimeric form.Under normal conditions TFF3 is expressed by goblet cells of theintestine and the colon. TFF3 expression has also been observed in thehuman respiratory tract, in human goblet cells and in the human salivarygland. In addition, TFF3 has been detected in the human hypothalamus.

(p) Vascular Endothelial Growth Factor (VEGF)

Vascular endothelial growth factor (VEGF, Swiss-Prot Accession NumberP15692) is an important factor in the pathophysiology of neuronal andother tumors, most likely functioning as a potent promoter ofangiogenesis. VEGF may also be involved in regulatingblood-brain-barrier functions under normal and pathological conditions.VEGF secreted from the stromal cells may be responsible for theendothelial cell proliferation observed in capillary hemangioblastomas,which are typically composed of abundant microvasculature and primitiveangiogenic elements represented by stromal cells.

II. Combinations of Analytes Measured by Multiplexed Assay

The method for diagnosing, monitoring, or determining kidney damageinvolves determining the presence or concentrations of a combination ofsample analytes in a test sample. The combinations of sample analytes,as defined herein, are any group of three or more analytes selected fromthe biomarker analytes, including but not limited to alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF. In one embodiment, thecombination of analytes may be selected to provide a group of analytesassociated with a wide range of potential types of kidney damage. Inanother embodiment, the combination of analytes may be selected toprovide a group of analytes associated with a particular type of kidneydamage or region of renal injury.

In one embodiment, the combination of sample analytes may be any threeof the biomarker analytes. In other embodiments, the combination ofsample analytes may be any four, any five, any six, any seven, anyeight, any nine, any ten, any eleven, any twelve, any thirteen, anyfourteen, any fifteen, or all sixteen of the sixteen biomarker analytes.In some embodiments, the combination of sample analytes comprisesalpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, andTIMP-1. In another embodiment, the combination of sample analytes maycomprise a combination listed in Table A.

TABLE A alpha-1 microglobulin beta-2 microglobulin calbindin alpha-1microglobulin beta-2 microglobulin clusterin alpha-1 microglobulinbeta-2 microglobulin CTGF alpha-1 microglobulin beta-2 microglobulincreatinine alpha-1 microglobulin beta-2 microglobulin cystatin C alpha-1microglobulin beta-2 microglobulin GST-alpha alpha-1 microglobulinbeta-2 microglobulin KIM-1 alpha-1 microglobulin beta-2 microglobulinmicroalbumin alpha-1 microglobulin beta-2 microglobulin NGAL alpha-1microglobulin beta-2 microglobulin osteopontin alpha-1 microglobulinbeta-2 microglobulin THP alpha-1 microglobulin beta-2 microglobulinTIMP-1 alpha-1 microglobulin beta-2 microglobulin TFF-3 alpha-1microglobulin beta-2 microglobulin VEGF alpha-1 microglobulin calbindinclusterin alpha-1 microglobulin calbindin CTGF alpha-1 microglobulincalbindin creatinine alpha-1 microglobulin calbindin cystatin C alpha-1microglobulin calbindin GST-alpha alpha-1 microglobulin calbindin KIM-1alpha-1 microglobulin calbindin microalbumin alpha-1 microglobulincalbindin NGAL alpha-1 microglobulin calbindin osteopontin alpha-1microglobulin calbindin THP alpha-1 microglobulin calbindin TIMP-1alpha-1 microglobulin calbindin TFF-3 alpha-1 microglobulin calbindinVEGF alpha-1 microglobulin clusterin CTGF alpha-1 microglobulinclusterin creatinine alpha-1 microglobulin clusterin cystatin C alpha-1microglobulin clusterin GST-alpha alpha-1 microglobulin clusterin KIM-1alpha-1 microglobulin clusterin microalbumin alpha-1 microglobulinclusterin NGAL alpha-1 microglobulin clusterin osteopontin alpha-1microglobulin clusterin THP alpha-1 microglobulin clusterin TIMP-1alpha-1 microglobulin clusterin TFF-3 alpha-1 microglobulin clusterinVEGF alpha-1 microglobulin CTGF creatinine alpha-1 microglobulin CTGFcystatin C alpha-1 microglobulin CTGF GST-alpha alpha-1 microglobulinCTGF KIM-1 alpha-1 microglobulin CTGF microalbumin alpha-1 microglobulinCTGF NGAL alpha-1 microglobulin CTGF osteopontin alpha-1 microglobulinCTGF THP alpha-1 microglobulin CTGF TIMP-1 alpha-1 microglobulin CTGFTFF-3 alpha-1 microglobulin CTGF VEGF alpha-1 microglobulin creatininecystatin C alpha-1 microglobulin creatinine GST-alpha alpha-1microglobulin creatinine KIM-1 alpha-1 microglobulin creatininemicroalbumin alpha-1 microglobulin creatinine NGAL alpha-1 microglobulincreatinine osteopontin alpha-1 microglobulin creatinine THP alpha-1microglobulin creatinine TIMP-1 alpha-1 microglobulin creatinine TFF-3alpha-1 microglobulin creatinine VEGF alpha-1 microglobulin cystatin CGST-alpha alpha-1 microglobulin cystatin C KIM-1 alpha-1 microglobulincystatin C microalbumin alpha-1 microglobulin cystatin C NGAL alpha-1microglobulin cystatin C osteopontin alpha-1 microglobulin cystatin CTHP alpha-1 microglobulin cystatin C TIMP-1 alpha-1 microglobulincystatin C TFF-3 alpha-1 microglobulin cystatin C VEGF alpha-1microglobulin GST-alpha KIM-1 alpha-1 microglobulin GST-alphamicroalbumin alpha-1 microglobulin GST-alpha NGAL alpha-1 microglobulinGST-alpha osteopontin alpha-1 microglobulin GST-alpha THP alpha-1microglobulin GST-alpha TIMP-1 alpha-1 microglobulin GST-alpha TFF-3alpha-1 microglobulin GST-alpha VEGF alpha-1 microglobulin KIM-1microalbumin alpha-1 microglobulin KIM-1 NGAL alpha-1 microglobulinKIM-1 osteopontin alpha-1 microglobulin KIM-1 THP alpha-1 microglobulinKIM-1 TIMP-1 alpha-1 microglobulin KIM-1 TFF-3 alpha-1 microglobulinKIM-1 VEGF alpha-1 microglobulin microalbumin NGAL alpha-1 microglobulinmicroalbumin osteopontin alpha-1 microglobulin microalbumin THP alpha-1microglobulin microalbumin TIMP-1 alpha-1 microglobulin microalbuminTFF-3 alpha-1 microglobulin microalbumin VEGF alpha-1 microglobulin NGALosteopontin alpha-1 microglobulin NGAL THP alpha-1 microglobulin NGALTIMP-1 alpha-1 microglobulin NGAL TFF-3 alpha-1 microglobulin NGAL VEGFalpha-1 microglobulin osteopontin THP alpha-1 microglobulin osteopontinTIMP-1 alpha-1 microglobulin osteopontin TFF-3 alpha-1 microglobulinosteopontin VEGF alpha-1 microglobulin THP TIMP-1 alpha-1 microglobulinTHP TFF-3 alpha-1 microglobulin THP VEGF alpha-1 microglobulin TIMP-1TFF-3 alpha-1 microglobulin TIMP-1 VEGF alpha-1 microglobulin TFF-3 VEGFbeta-2 microglobulin calbindin clusterin beta-2 microglobulin calbindinCTGF beta-2 microglobulin calbindin creatinine beta-2 microglobulincalbindin cystatin C beta-2 microglobulin calbindin GST-alpha beta-2microglobulin calbindin KIM-1 beta-2 microglobulin calbindinmicroalbumin beta-2 microglobulin calbindin NGAL beta-2 microglobulincalbindin osteopontin beta-2 microglobulin calbindin THP beta-2microglobulin calbindin TIMP-1 beta-2 microglobulin calbindin TFF-3beta-2 microglobulin calbindin VEGF beta-2 microglobulin clusterin CTGFbeta-2 microglobulin clusterin creatinine beta-2 microglobulin clusterincystatin C beta-2 microglobulin clusterin GST-alpha beta-2 microglobulinclusterin KIM-1 beta-2 microglobulin clusterin microalbumin beta-2microglobulin clusterin NGAL beta-2 microglobulin clusterin osteopontinbeta-2 microglobulin clusterin THP beta-2 microglobulin clusterin TIMP-1beta-2 microglobulin clusterin TFF-3 beta-2 microglobulin clusterin VEGFbeta-2 microglobulin CTGF creatinine beta-2 microglobulin CTGF cystatinC beta-2 microglobulin CTGF GST-alpha beta-2 microglobulin CTGF KIM-1beta-2 microglobulin CTGF microalbumin beta-2 microglobulin CTGF NGALbeta-2 microglobulin CTGF osteopontin beta-2 microglobulin CTGF THPbeta-2 microglobulin CTGF TIMP-1 beta-2 microglobulin CTGF TFF-3 beta-2microglobulin CTGF VEGF beta-2 microglobulin creatinine cystatin Cbeta-2 microglobulin creatinine GST-alpha beta-2 microglobulincreatinine KIM-1 beta-2 microglobulin creatinine microalbumin beta-2microglobulin creatinine NGAL beta-2 microglobulin creatinineosteopontin beta-2 microglobulin creatinine THP beta-2 microglobulincreatinine TIMP-1 beta-2 microglobulin creatinine TFF-3 beta-2microglobulin creatinine VEGF beta-2 microglobulin cystatin C GST-alphabeta-2 microglobulin cystatin C KIM-1 beta-2 microglobulin cystatin Cmicroalbumin beta-2 microglobulin cystatin C NGAL beta-2 microglobulincystatin C osteopontin beta-2 microglobulin cystatin C THP beta-2microglobulin cystatin C TIMP-1 beta-2 microglobulin cystatin C TFF-3beta-2 microglobulin cystatin C VEGF beta-2 microglobulin GST-alphaKIM-1 beta-2 microglobulin GST-alpha microalbumin beta-2 microglobulinGST-alpha NGAL beta-2 microglobulin GST-alpha osteopontin beta-2microglobulin GST-alpha THP beta-2 microglobulin GST-alpha TIMP-1 beta-2microglobulin GST-alpha TFF-3 beta-2 microglobulin GST-alpha VEGF beta-2microglobulin KIM-1 microalbumin beta-2 microglobulin KIM-1 NGAL beta-2microglobulin KIM-1 osteopontin beta-2 microglobulin KIM-1 THP beta-2microglobulin KIM-1 TIMP-1 beta-2 microglobulin KIM-1 TFF-3 beta-2microglobulin KIM-1 VEGF beta-2 microglobulin microalbumin NGAL beta-2microglobulin microalbumin osteopontin beta-2 microglobulin microalbuminTHP beta-2 microglobulin microalbumin TIMP-1 beta-2 microglobulinmicroalbumin TFF-3 beta-2 microglobulin microalbumin VEGF beta-2microglobulin NGAL osteopontin beta-2 microglobulin NGAL THP beta-2microglobulin NGAL TIMP-1 beta-2 microglobulin NGAL TFF-3 beta-2microglobulin NGAL VEGF beta-2 microglobulin osteopontin THP beta-2microglobulin osteopontin TIMP-1 beta-2 microglobulin osteopontin TFF-3beta-2 microglobulin osteopontin VEGF beta-2 microglobulin THP TIMP-1beta-2 microglobulin THP TFF-3 beta-2 microglobulin THP VEGF beta-2microglobulin TIMP-1 TFF-3 beta-2 microglobulin TIMP-2 VEGF beta-2microglobulin TFF-3 VEGF calbindin clusterin CTGF calbindin clusterincreatinine calbindin clusterin cystatin C calbindin clusterin GST-alphacalbindin clusterin KIM-1 calbindin clusterin microalbumin calbindinclusterin NGAL calbindin clusterin osteopontin calbindin clusterin THPcalbindin clusterin TIMP-1 calbindin clusterin TFF-3 calbindin clusterinVEGF calbindin CTGF creatinine calbindin CTGF cystatin C calbindin CTGFGST-alpha calbindin CTGF KIM-1 calbindin CTGF microalbumin calbindinCTGF NGAL calbindin CTGF osteopontin calbindin CTGF THP calbindin CTGFTIMP-1 calbindin CTGF TFF-3 calbindin CTGF VEGF calbindin creatininecystatin C calbindin creatinine GST-alpha calbindin creatinine KIM-1calbindin creatinine microalbumin calbindin creatinine NGAL calbindincreatinine osteopontin calbindin creatinine THP calbindin creatinineTIMP-1 calbindin creatinine TFF-3 calbindin creatinine VEGF calbindincystatin C GST-alpha calbindin cystatin C KIM-1 calbindin cystatin Cmicroalbumin calbindin cystatin C NGAL calbindin cystatin C osteopontincalbindin cystatin C THP calbindin cystatin C TIMP-1 calbindin cystatinC TFF-3 calbindin cystatin C VEGF calbindin GST-alpha KIM-1 calbindinGST-alpha microalbumin calbindin GST-alpha NGAL calbindin GST-alphaosteopontin calbindin GST-alpha THP calbindin GST-alpha TIMP-1 calbindinGST-alpha TFF-3 calbindin GST-alpha VEGF calbindin KIM-1 microalbumincalbindin KIM-1 NGAL calbindin KIM-1 osteopontin calbindin KIM-1 THPcalbindin KIM-1 TIMP-1 calbindin KIM-1 TFF-3 calbindin KIM-1 VEGFcalbindin microalbumin NGAL calbindin microalbumin osteopontin calbindinmicroalbumin THP calbindin microalbumin TIMP-1 calbindin microalbuminTFF-3 calbindin microalbumin VEGF calbindin NGAL osteopontin calbindinNGAL THP calbindin NGAL TIMP-1 calbindin NGAL TFF-3 calbindin NGAL VEGFcalbindin osteopontin THP calbindin osteopontin TIMP-1 calbindinosteopontin TFF-3 calbindin osteopontin VEGF calbindin THP TIMP-1calbindin THP TFF-3 calbindin THP VEGF calbindin TIMP-1 TFF-3 calbindinTIMP-1 VEGF calbindin TFF-3 VEGF clusterin CTGF creatinine clusterinCTGF cystatin C clusterin CTGF GST-alpha clusterin CTGF KIM-1 clusterinCTGF microalbumin clusterin CTGF NGAL clusterin CTGF osteopontinclusterin CTGF THP clusterin CTGF TIMP-1 clusterin CTGF TFF-3 clusterinCTGF VEGF clusterin creatinine cystatin C clusterin creatinine GST-alphaclusterin creatinine KIM-1 clusterin creatinine microalbumin clusterincreatinine NGAL clusterin creatinine osteopontin clusterin creatinineTHP clusterin creatinine TIMP-1 clusterin creatinine TFF-3 clusterincreatinine VEGF clusterin cystatin C GST-alpha clusterin cystatin CKIM-1 clusterin cystatin C microalbumin clusterin cystatin C NGALclusterin cystatin C osteopontin clusterin cystatin C THP clusterincystatin C TIMP-1 clusterin cystatin C TFF-3 clusterin cystatin C VEGFclusterin GST-alpha KIM-1 clusterin GST-alpha microalbumin clusterinGST-alpha NGAL clusterin GST-alpha osteopontin clusterin GST-alpha THPclusterin GST-alpha TIMP-1 clusterin GST-alpha TFF-3 clusterin GST-alphaVEGF clusterin KIM-1 microalbumin clusterin KIM-1 NGAL clusterin KIM-1osteopontin clusterin KIM-1 THP clusterin KIM-1 TIMP-1 clusterin KIM-1TFF-3 clusterin KIM-1 VEGF clusterin microalbumin NGAL clusterinmicroalbumin osteopontin clusterin microalbumin THP clusterinmicroalbumin TIMP-1 clusterin microalbumin TFF-3 clusterin microalbuminVEGF clusterin NGAL osteopontin clusterin NGAL THP clusterin NGAL TIMP-1clusterin NGAL TFF-3 clusterin NGAL VEGF clusterin osteopontin THPclusterin osteopontin TIMP-1 clusterin osteopontin TFF-3 clusterinosteopontin VEGF clusterin THP TIMP-1 clusterin THP TFF-3 clusterin THPVEGF clusterin TIMP-1 TFF-3 clusterin TIMP-1 VEGF clusterin TFF-3 VEGFCTGF creatinine cystatin C CTGF creatinine GST-alpha CTGF creatinineKIM-1 CTGF creatinine microalbumin CTGF creatinine NGAL CTGF creatinineosteopontin CTGF creatinine THP CTGF creatinine TIMP-1 CTGF creatinineTFF-3 CTGF creatinine VEGF CTGF cystatin C GST-alpha CTGF cystatin CKIM-1 CTGF cystatin C microalbumin CTGF cystatin C NGAL CTGF cystatin Costeopontin CTGF cystatin C THP CTGF cystatin C TIMP-1 CTGF cystatin CTFF-3 CTGF cystatin C VEGF CTGF GST-alpha KIM-1 CTGF GST-alphamicroalbumin CTGF GST-alpha NGAL CTGF GST-alpha osteopontin CTGFGST-alpha THP CTGF GST-alpha TIMP-1 CTGF GST-alpha TFF-3 CTGF GST-alphaVEGF CTGF KIM-1 microalbumin CTGF KIM-1 NGAL CTGF KIM-1 osteopontin CTGFKIM-1 THP CTGF KIM-1 TIMP-1 CTGF KIM-1 TFF-3 CTGF KIM-1 VEGF CTGFmicroalbumin NGAL CTGF microalbumin osteopontin CTGF microalbumin THPCTGF microalbumin TIMP-1 CTGF microalbumin TFF-3 CTGF microalbumin VEGFCTGF NGAL osteopontin CTGF NGAL THP CTGF NGAL TIMP-1 CTGF NGAL TFF-3CTGF NGAL VEGF CTGF osteopontin THP CTGF osteopontin TIMP-1 CTGFosteopontin TFF-3 CTGF osteopontin VEGF CTGF THP TIMP-1 CTGF THP TFF-3CTGF THP VEGF CTGF TIMP-1 TFF-3 CTGF TIMP-1 VEGF CTGF TFF-3 VEGFcreatinine cystatin C GST-alpha creatinine cystatin C KIM-1 creatininecystatin C microalbumin creatinine cystatin C NGAL creatinine cystatin Costeopontin creatinine cystatin C THP creatinine cystatin C TIMP-1creatinine cystatin C TFF-3 creatinine cystatin C VEGF creatinineGST-alpha KIM-1 creatinine GST-alpha microalbumin creatinine GST-alphaNGAL creatinine GST-alpha osteopontin creatinine GST-alpha THPcreatinine GST-alpha TIMP-1 creatinine GST-alpha TFF-3 creatinineGST-alpha VEGF creatinine KIM-1 microalbumin creatinine KIM-1 NGALcreatinine KIM-1 osteopontin creatinine KIM-1 THP creatinine KIM-1TIMP-1 creatinine KIM-1 TFF-3 creatinine KIM-1 VEGF creatininemicroalbumin NGAL creatinine microalbumin osteopontin creatininemicroalbumin THP creatinine microalbumin TIMP-1 creatinine microalbuminTFF-3 creatinine microalbumin VEGF creatinine NGAL osteopontincreatinine NGAL THP creatinine NGAL TIMP-1 creatinine NGAL TFF-3creatinine NGAL VEGF creatinine osteopontin THP creatinine osteopontinTIMP-1 creatinine osteopontin TFF-3 creatinine osteopontin VEGFcreatinine THP TIMP-1 creatinine THP TFF-3 creatinine THP VEGFcreatinine TIMP-1 TFF-3 creatinine TIMP-1 VEGF creatinine TFF-3 VEGFcystatin C GST-alpha KIM-1 cystatin C GST-alpha microalbumin cystatin CGST-alpha NGAL cystatin C GST-alpha osteopontin cystatin C GST-alpha THPcystatin C GST-alpha TIMP-1 cystatin C GST-alpha TFF-3 cystatin CGST-alpha VEGF cystatin C KIM-1 microalbumin cystatin C KIM-1 NGALcystatin C KIM-1 osteopontin cystatin C KIM-1 THP cystatin C KIM-1TIMP-1 cystatin C KIM-1 TFF-3 cystatin C KIM-1 VEGF cystatin Cmicroalbumin NGAL cystatin C microalbumin osteopontin cystatin Cmicroalbumin THP cystatin C microalbumin TIMP-1 cystatin C microalbuminTFF-3 cystatin C microalbumin VEGF cystatin C NGAL osteopontin cystatinC NGAL THP cystatin C NGAL TIMP-1 cystatin C NGAL TFF-3 cystatin C NGALVEGF cystatin C osteopontin THP cystatin C osteopontin TIMP-1 cystatin Costeopontin TFF-3 cystatin C osteopontin VEGF cystatin C THP TIMP-1cystatin C THP TFF-3 cystatin C THP VEGF cystatin C TIMP-1 TFF-3cystatin C TIMP-1 VEGF cystatin C TFF-3 VEGF GST-alpha KIM-1microalbumin GST-alpha KIM-1 NGAL GST-alpha KIM-1 osteopontin GST-alphaKIM-1 THP GST-alpha KIM-1 TIMP-1 GST-alpha KIM-1 TFF-3 GST-alpha KIM-1VEGF GST-alpha microalbumin NGAL GST-alpha microalbumin osteopontinGST-alpha microalbumin THP GST-alpha microalbumin TIMP-1 GST-alphamicroalbumin TFF-3 GST-alpha microalbumin VEGF GST-alpha NGALosteopontin GST-alpha NGAL THP GST-alpha NGAL TIMP-1 GST-alpha NGALTFF-3 GST-alpha NGAL VEGF GST-alpha osteopontin THP GST-alphaosteopontin TIMP-1 GST-alpha osteopontin TFF-3 GST-alpha osteopontinVEGF GST-alpha THP TIMP-1 GST-alpha THP TFF-3 GST-alpha THP VEGFGST-alpha TIMP-1 TFF-3 GST-alpha TIMP-1 VEGF GST-alpha TFF-3 VEGF KIM-1microalbumin NGAL KIM-1 microalbumin osteopontin KIM-1 microalbumin THPKIM-1 microalbumin TIMP-1 KIM-1 microalbumin TFF-3 KIM-1 microalbuminVEGF KIM-1 NGAL osteopontin KIM-1 NGAL THP KIM-1 NGAL TIMP-1 KIM-1 NGALTFF-3 KIM-1 NGAL VEGF KIM-1 osteopontin THP KIM-1 osteopontin TIMP-1KIM-1 osteopontin TFF-3 KIM-1 osteopontin VEGF KIM-1 THP TIMP-1 KIM-1THP TFF-3 KIM-1 THP VEGF KIM-1 TIMP-1 TFF-3 KIM-1 TIMP-1 VEGF KIM-1TFF-3 VEGF microalbumin NGAL osteopontin microalbumin NGAL THPmicroalbumin NGAL TIMP-1 microalbumin NGAL TFF-3 microalbumin NGAL VEGFmicroalbumin osteopontin THP microalbumin osteopontin TIMP-1microalbumin osteopontin TFF-3 microalbumin osteopontin VEGFmicroalbumin THP TIMP-1 microalbumin THP TFF-3 microalbumin THP VEGFmicroalbumin TIMP-1 TFF-3 microalbumin TIMP-1 VEGF microalbumin TFF-3VEGF NGAL osteopontin THP NGAL osteopontin TIMP-1 NGAL osteopontin TFF-3NGAL osteopontin VEGF NGAL THP TIMP-1 NGAL THP TFF-3 NGAL THP VEGF NGALTIMP-1 TFF-3 NGAL TIMP-1 VEGF NGAL TFF-3 VEGF osteopontin THP TIMP-1osteopontin THP TFF-3 osteopontin THP VEGF osteopontin TIMP-1 TFF-3osteopontin TIMP-1 VEGF osteopontin TFF-3 VEGF THP TIMP-1 TFF-3 THPTIMP-1 VEGF THP TFF-3 VEGF TIMP-1 TFF-3 VEGF

In one exemplary embodiment, the combination of sample analytes mayinclude creatinine, KIM-1, and THP. In another exemplary embodiment, thecombination of sample analytes may include microalbumin, creatinine, andKIM-1. In yet another exemplary embodiment, the combination of sampleanalytes may include creatinine, TIMP-1, and THP. In still anotherexemplary embodiment, the combination of sample analytes may includecreatinine, microalbumin, and THP.

III. Test Sample

The method for diagnosing, monitoring, or determining a renal disorderinvolves determining the presence of sample analytes in a test sample. Atest sample, as defined herein, is an amount of bodily fluid taken froma mammal. Non-limiting examples of bodily fluids include urine, blood,plasma, serum, saliva, semen, perspiration, tears, mucus, and tissuelysates. In an exemplary embodiment, the bodily fluid contained in thetest sample is urine, plasma, or serum.

(a) Mammals

A mammal, as defined herein, is any organism that is a member of theclass Mammalia. Non-limiting examples of mammals appropriate for thevarious embodiments include humans, apes, monkeys, rats, mice, dogs,cats, pigs, and livestock including cattle and oxen. In an exemplaryembodiment, the mammal is a human.

(b) Devices and Methods of Taking Bodily Fluids from Mammals

The bodily fluids of the test sample may be taken from the mammal usingany known device or method so long as the analytes to be measured by themultiplexed assay are not rendered undetectable by the multiplexedassay. Non-limiting examples of devices or methods suitable for takingbodily fluid from a mammal include urine sample cups, urethralcatheters, swabs, hypodermic needles, thin needle biopsies, hollowneedle biopsies, punch biopsies, metabolic cages, and aspiration.

In order to adjust the expected concentrations of the sample analytes inthe test sample to fall within the dynamic range of the multiplexedassay, the test sample may be diluted to reduce the concentration of thesample analytes prior to analysis. The degree of dilution may depend ona variety of factors including but not limited to the type ofmultiplexed assay used to measure the analytes, the reagents utilized inthe multiplexed assay, and the type of bodily fluid contained in thetest sample. In one embodiment, the test sample is diluted by adding avolume of diluent ranging from about ½ of the original test samplevolume to about 50,000 times the original test sample volume.

In one exemplary embodiment, if the test sample is human urine and themultiplexed assay is an antibody-based capture-sandwich assay, the testsample is diluted by adding a volume of diluent that is about 100 timesthe original test sample volume prior to analysis. In another exemplaryembodiment, if the test sample is human serum and the multiplexed assayis an antibody-based capture-sandwich assay, the test sample is dilutedby adding a volume of diluent that is about 5 times the original testsample volume prior to analysis. In yet another exemplary embodiment, ifthe test sample is human plasma and the multiplexed assay is anantibody-based capture-sandwich assay, the test sample is diluted byadding a volume of diluent that is about 2,000 times the original testsample volume prior to analysis.

The diluent may be any fluid that does not interfere with the functionof the multiplexed assay used to measure the concentration of theanalytes in the test sample. Non-limiting examples of suitable diluentsinclude deionized water, distilled water, saline solution, Ringer'ssolution, phosphate buffered saline solution, TRIS-buffered salinesolution, standard saline citrate, and HEPES-buffered saline.

IV. Multiplexed Assay Device

In one embodiment, the concentration of a combination of sample analytesis measured using a multiplexed assay device capable of measuring theconcentrations of up to sixteen of the biomarker analytes. A multiplexedassay device, as defined herein, is an assay capable of simultaneouslydetermining the concentration of three or more different sample analytesusing a single device and/or method. Any known method of measuring theconcentration of the biomarker analytes may be used for the multiplexedassay device. Non-limiting examples of measurement methods suitable forthe multiplexed assay device include electrophoresis, mass spectrometry,protein microarrays, and immunoassays including but not limited towestern blot, immunohistochemical staining, enzyme-linked immunosorbentassay (ELISA) methods, and particle-based capture-sandwich immunoassays.

(a) Multiplexed Immunoassay Device

In one embodiment, the concentration of a combination of sample analytesis measured using a multiplexed assay device capable of measuring theconcentrations of up to 189 of the biomarker analytes. A multiplexedassay device, as defined herein, is an assay capable of simultaneouslydetermining the concentration of three or more different sample analytesusing a single device and/or method. Any known method of measuring theconcentration of the biomarker analytes may be used for the multiplexedassay device. Non-limiting examples of measurement methods suitable forthe multiplexed assay device include electrophoresis, mass spectrometry,protein microarrays, and immunoassays including but not limited towestern blot, immunohistochemical staining, enzyme-linked immunosorbentassay (ELISA) methods, vibrational detection using MicroElectroMagneticDevices (MEMS), and particle-based capture-sandwich immunoassays.

(i) Capture Antibodies

In the same embodiment, the multiplexed immunoassay device includesthree or more capture antibodies. Capture antibodies, as defined herein,are antibodies in which the antigenic determinant is one of thebiomarker analytes. Each of the at least three capture antibodies has aunique antigenic determinant that is one of the biomarker analytes. Whencontacted with the test sample, the capture antibodies formantigen-antibody complexes in which the analytes serve as antigens.

In another embodiment, the capture antibodies may be attached to asubstrate in order to immobilize any analytes captured by the captureantibodies. Non-limiting examples of suitable substrates include paperor cellulose strips, polystyrene or latex microspheres, and the innersurface of the well of a microtitration tray.

(ii) Indicators

In one embodiment of the multiplexed immunoassay device, an indicator isattached to each of the three or more capture antibodies. The indicator,as defined herein, is any compound that registers a measurable change toindicate the presence of one of the sample analytes when bound to one ofthe capture antibodies. Non-limiting examples of indicators includevisual indicators and electrochemical indicators.

Visual indicators, as defined herein, are compounds that register achange by reflecting a limited subset of the wavelengths of lightilluminating the indicator, by fluorescing light after beingilluminated, or by emitting light via chemiluminescence. The changeregistered by visual indicators may be in the visible light spectrum, inthe infrared spectrum, or in the ultraviolet spectrum. Non-limitingexamples of visual indicators suitable for the multiplexed immunoassaydevice include nanoparticulate gold, organic particles such aspolyurethane or latex microspheres loaded with dye compounds, carbonblack, fluorophores, phycoerythrin, radioactive isotopes, nanoparticles,quantum dots, and enzymes such as horseradish peroxidase or alkalinephosphatase that react with a chemical substrate to form a colored orchemiluminescent product.

Electrochemical indicators, as defined herein, are compounds thatregister a change by altering an electrical property. The changesregistered by electrochemical indicators may be an alteration inconductivity, resistance, capacitance, current conducted in response toan applied voltage, or voltage required to achieve a desired current.Non-limiting examples of electrochemical indicators include redoxspecies such as ascorbate (vitamin C), vitamin E, glutathione,polyphenols, catechols, quercetin, phytoestrogens, penicillin,carbazole, murranes, phenols, carbonyls, benzoates, and trace metal ionssuch as nickel, copper, cadmium, iron and mercury.

In this same embodiment, the test sample containing a combination ofthree or more sample analytes is contacted with the capture antibodiesand allowed to form antigen-antibody complexes in which the sampleanalytes serve as the antigens. After removing any uncomplexed captureantibodies, the concentrations of the three or more analytes aredetermined by measuring the change registered by the indicators attachedto the capture antibodies.

In one exemplary embodiment, the indicators are polyurethane or latexmicrospheres loaded with dye compounds and phycoerythrin.

(b) Multiplexed Sandwich Immunoassay Device

In another embodiment, the multiplexed immunoassay device has a sandwichassay format. In this embodiment, the multiplexed sandwich immunoassaydevice includes three or more capture antibodies as previouslydescribed. However, in this embodiment, each of the capture antibodiesis attached to a capture agent that includes an antigenic moiety. Theantigenic moiety serves as the antigenic determinant of a detectionantibody, also included in the multiplexed immunoassay device of thisembodiment. In addition, an indicator is attached to the detectionantibody.

In this same embodiment, the test sample is contacted with the captureantibodies and allowed to form antigen-antibody complexes in which thesample analytes serve as antigens. The detection antibodies are thencontacted with the test sample and allowed to form antigen-antibodycomplexes in which the capture agent serves as the antigen for thedetection antibody. After removing any uncomplexed detection antibodiesthe concentration of the analytes are determined by measuring thechanges registered by the indicators attached to the detectionantibodies.

(c) Multiplexing Approaches

In the various embodiments of the multiplexed immunoassay devices, theconcentrations of each of the sample analytes may be determined usingany approach known in the art. In one embodiment, a single indicatorcompound is attached to each of the three or more antibodies. Inaddition, each of the capture antibodies having one of the sampleanalytes as an antigenic determinant is physically separated into adistinct region so that the concentration of each of the sample analytesmay be determined by measuring the changes registered by the indicatorsin each physically separate region corresponding to each of the sampleanalytes.

In another embodiment, each antibody having one of the sample analytesas an antigenic determinant is marked with a unique indicator. In thismanner, a unique indicator is attached to each antibody having a singlesample analyte as its antigenic determinant. In this embodiment, allantibodies may occupy the same physical space. The concentration of eachsample analyte is determined by measuring the change registered by theunique indicator attached to the antibody having the sample analyte asan antigenic determinant.

(d) Microsphere-Based Capture-Sandwich Immunoassay Device

In an exemplary embodiment, the multiplexed immunoassay device is amicrosphere-based capture-sandwich immunoassay device. In thisembodiment, the device includes a mixture of three or morecapture-antibody microspheres, in which each capture-antibodymicrosphere corresponds to one of the biomarker analytes. Eachcapture-antibody microsphere includes a plurality of capture antibodiesattached to the outer surface of the microsphere. In this sameembodiment, the antigenic determinant of all of the capture antibodiesattached to one microsphere is the same biomarker analyte.

In this embodiment of the device, the microsphere is a small polystyreneor latex sphere that is loaded with an indicator that is a dye compound.The microsphere may be between about 3 μm and about 5 μm in diameter.Each capture-antibody microsphere corresponding to one of the biomarkeranalytes is loaded with the same indicator. In this manner, eachcapture-antibody microsphere corresponding to a biomarker analyte isuniquely color-coded.

In this same exemplary embodiment, the multiplexed immunoassay devicefurther includes three or more biotinylated detection antibodies inwhich the antigenic determinant of each biotinylated detection antibodyis one of the biomarker analytes. The device further includes aplurality of streptaviden proteins complexed with a reporter compound. Areporter compound, as defined herein, is an indicator selected toregister a change that is distinguishable from the indicators used tomark the capture-antibody microspheres.

The concentrations of the sample analytes may be determined bycontacting the test sample with a mixture of capture-antigenmicrospheres corresponding to each sample analyte to be measured. Thesample analytes are allowed to form antigen-antibody complexes in whicha sample analyte serves as an antigen and a capture antibody attached tothe microsphere serves as an antibody. In this manner, the sampleanalytes are immobilized onto the capture-antigen microspheres. Thebiotinylated detection antibodies are then added to the test sample andallowed to form antigen-antibody complexes in which the analyte servesas the antigen and the biotinylated detection antibody serves as theantibody. The streptaviden-reporter complex is then added to the testsample and allowed to bind to the biotin moieties of the biotinylateddetection antibodies. The antigen-capture microspheres may then berinsed and filtered.

In this embodiment, the concentration of each analyte is determined byfirst measuring the change registered by the indicator compound embeddedin the capture-antigen microsphere in order to identify the particularanalyte. For each microsphere corresponding to one of the biomarkeranalytes, the quantity of analyte immobilized on the microsphere isdetermined by measuring the change registered by the reporter compoundattached to the microsphere.

For example, the indicator embedded in the microspheres associated withone sample analyte may register an emission of orange light, and thereporter may register an emission of green light. In this example, adetector device may measure the intensity of orange light and greenlight separately. The measured intensity of the green light woulddetermine the concentration of the analyte captured on the microsphere,and the intensity of the orange light would determine the specificanalyte captured on the microsphere.

Any sensor device may be used to detect the changes registered by theindicators embedded in the microspheres and the changes registered bythe reporter compound, so long as the sensor device is sufficientlysensitive to the changes registered by both indicator and reportercompound. Non-limiting examples of suitable sensor devices includespectrophotometers, photosensors, colorimeters, cyclic coulometrydevices, and flow cytometers. In an exemplary embodiment, the sensordevice is a flow cytometer.

(e) Vibrational Detection Device

In another exemplary embodiment, the multiplexed immunoassay device hasa vibrational detection format using a MEMS. In this embodiment, theimmunoassay device uses capture antibodies as previously described.However, in this embodiment, the capture antibodies are attached to amicroscopic silicon microcantilever beam structure. The microcantileversare micromechanical beams that are anchored at one end, such as divingspring boards that can be readily fabricated on silicon wafers and othermaterials. The microcantilever sensors are physical sensors that respondto surface stress changes due to chemical or biological processes. Whenfabricated with very small force constants, they can measure forces andstresses with extremely high sensitivity. The very small force constantof a cantilever allows detection not surface stress variation due to thebinding of an analyte to the capture antibody on the microcantilever.Binding of the analyte results in a differential surface stress due toadsorption-induced forces, which manifests as a deflection which can bemeasured. The vibrational detection may be multiplexed. For moredetails, see Datar et al., MRS Bulletin (2009) 34:449-459 and Gaster etal., Nature Medicine (2009) 15:1327-1332, both of which are herebyincorporated by reference in their entireties.

V. Method for Diagnosing, Monitoring, or Determining a Renal Disorder

In one embodiment, a method is provided for diagnosing, monitoring, ordetermining a renal disorder that includes providing a test sample,determining the concentration of a combination of three or more a sampleanalytes, comparing the measured concentrations of the combination ofsample analytes to the entries of a dataset, and identifying aparticular renal disorder based on the comparison between theconcentrations of the sample analytes and the minimum diagnosticconcentrations contained within each entry of the dataset.

(a) Diagnostic Dataset

In an embodiment, the concentrations of the sample analytes are comparedto the entries of a dataset. In this embodiment, each entry of thedataset includes a combination of three or more minimum diagnosticconcentrations indicative of a particular renal disorder. A minimumdiagnostic concentration, as defined herein, is the concentration of ananalyte that defines the limit between the concentration rangecorresponding to normal, healthy renal function and the concentrationreflective of a particular renal disorder. In one embodiment, eachminimum diagnostic concentration is the maximum concentration of therange of analyte concentrations for a healthy, normal individual. Theminimum diagnostic concentration of an analyte depends on a number offactors including but not limited to the particular analyte and the typeof bodily fluid contained in the test sample. As an illustrativeexample, Table 1 lists the expected normal ranges of the biomarkeranalytes in human plasma, serum, and urine.

TABLE 1 Normal Concentration Ranges In Human Plasma, Serum, and UrineSamples Plasma Sera Urine Analyte Units low high low high low highCalbindin ng/ml — <5.0 — <2.6 4.2 233 Clusterin μg/ml 86 134 37 152 —<0.089 CTGF ng/ml 2.8 7.5 — <8.2 — <0.90 GST-alpha ng/ml 6.7 62 1.2 52 —<26 KIM-1 ng/ml 0.053 0.57 — <0.35 0.023 0.67 VEGF pg/ml 222 855 2191630 69 517 B2M μg/ml 0.68 2.2 1.00 2.6 <0.17 Cyst C ng/ml 608 1170 4761250 3.9 79 NGAL ng/ml 89 375 102 822 2.9 81 OPN ng/ml 4.1 25 0.49 12291 6130 TIMP-1 ng/ml 50 131 100 246 — <3.9 A1M μg/ml 6.2 16 5.7 17 —<4.2 THP μg/ml 0.0084 0.052 0.0079 0.053 0.39 2.6 TFF3 μg/ml 0.040 0.490.021 0.17 — <21 Creatinine mg/dL — — — — 13 212 Microalbumin μg/ml — —— — — >16

In one embodiment, the high values shown for each of the biomarkeranalytes in Table 1 for the analytic concentrations in human plasma,sera and urine are the minimum diagnostics values for the analytes inhuman plasma, sera, and urine, respectively. In one exemplaryembodiment, the minimum diagnostic concentration in human plasma ofalpha-1 microglobulin is about 16 μg/ml, beta-2 microglobulin is about2.2 μg/ml, calbindin is greater than about 5 ng/ml, clusterin is about134 μg/ml, CTGF is about 16 ng/ml, cystatin C is about 1170 ng/ml,GST-alpha is about 62 ng/ml, KIM-1 is about 0.57 ng/ml, NGAL is about375 ng/ml, osteopontin is about 25 ng/ml, THP is about 0.052 μg/ml,TIMP-1 is about 131 ng/ml, TFF-3 is about 0.49 μg/ml, and VEGF is about855 μg/ml.

In another exemplary embodiment, the minimum diagnostic concentration inhuman sera of alpha-1 microglobulin is about 17 μg/ml, beta-2microglobulin is about 2.6 μg/ml, calbindin is greater than about 2.6ng/ml, clusterin is about 152 μg/ml, CTGF is greater than about 8.2ng/ml, cystatin C is about 1250 ng/ml, GST-alpha is about 52 ng/ml,KIM-1 is greater than about 0.35 ng/ml, NGAL is about 822 ng/ml,osteopontin is about 12 ng/ml, THP is about 0.053 μg/ml, TIMP-1 is about246 ng/ml, TFF-3 is about 0.17 μg/ml, and VEGF is about 1630 μg/ml.

In yet another exemplary embodiment, the minimum diagnosticconcentration in human urine of alpha-1 microglobulin is about 233μg/ml, beta-2 microglobulin is greater than about 0.17 μg/ml, calbindinis about 233 ng/ml, clusterin is greater than about 0.089 μg/ml, CTGF isgreater than about 0.90 ng/ml, cystatin C is about 1170 ng/ml, GST-alphais greater than about 26 ng/ml, KIM-1 is about 0.67 ng/ml, NGAL is about81 ng/ml, osteopontin is about 6130 ng/ml, THP is about 2.6 μg/ml,TIMP-1 is greater than about 3.9 ng/ml, TFF-3 is greater than about 21μg/ml, and VEGF is about 517 μg/ml.

In one embodiment, the minimum diagnostic concentrations represent themaximum level of analyte concentrations falling within an expectednormal range. A renal disorder may be indicated if the concentration ofan analyte is higher than the minimum diagnostic concentration for theanalyte.

If diminished concentrations of a particular analyte are known to beassociated with a particular renal disorder, the minimum diagnosticconcentration may not be an appropriate diagnostic criterion foridentifying the particular renal disorder indicated by the sampleanalyte concentrations. In these cases, a maximum diagnosticconcentration may define the limit between the expected normalconcentration range for the analyte and a sample concentrationreflective of a renal disorder. In those cases in which a maximumdiagnostic concentration is the appropriate diagnostic criterion, sampleconcentrations that fall below a maximum diagnostic concentration mayindicate a particular renal disorder.

A critical feature of the method of the multiplexed analyte panel isthat a combination of sample analyte concentrations may be used todiagnose a renal disorder. In addition to comparing subsets of thebiomarker analyte concentrations to diagnostic criteria, the analytesmay be algebraically combined and compared to corresponding diagnosticcriteria. In one embodiment, two or more sample analyte concentrationsmay be added and/or subtracted to determine a combined analyteconcentration. In another embodiment, two or more sample analyteconcentrations may be multiplied and/or divided to determine a combinedanalyte concentration. To identify a particular renal disorder, thecombined analyte concentration may be compared to a diagnostic criterionin which the corresponding minimum or maximum diagnostic concentrationsare combined using the same algebraic operations used to determine thecombined analyte concentration.

In yet another embodiment, the analyte concentration measured from atest sample containing one type of body fluid may be algebraicallycombined with an analyte concentration measured from a second testsample containing a second type of body fluid to determine a combinedanalyte concentration. For example, the ratio of urine calbindin toplasma calbindin may be determined and compared to a correspondingminimum diagnostic urine:plasma calbindin ratio to identify a particularrenal disorder.

A variety of methods known in the art may be used to define thediagnostic criteria used to identify a particular renal condition. Inone embodiment, any sample concentration falling outside the expectednormal range indicates a renal disorder. In another embodiment, themultiplexed analyte panel may be used to evaluate the analyteconcentrations in test samples taken from a population of patientshaving a particular renal disorder and compared to the normal expectedanalyte concentration ranges. In this same embodiment, any sampleanalyte concentrations that are significantly higher or lower than theexpected normal concentration range may be used to define a minimum ormaximum diagnostic concentration, respectively. A number of studiescomparing the biomarker concentration ranges of a population of patientshaving a renal disorder to the corresponding analyte concentrations froma population of normal healthy subjects are described in the examplessection below.

In another embodiment, the sample analyte concentrations of a populationof patients exposed to varying dosages of a potentially drug may becompared to each other and to the expected normal analyteconcentrations. Any sample analyte concentrations falling significantlyoutside the expected normal analyte concentration range may be used todefine diagnostic criteria. In addition, the sample analyteconcentrations may be correlated to the dosage of the potentially toxicdrug in order to define a diagnostic criteria used to determine theseverity of a particular renal disorder based on the sample analyteconcentration.

(b) Renal Disorders Associated with Minimum Diagnostic Concentrations inDiagnostic Dataset

A variety of renal disorders and locations of damage within the kidneymay be identified using a comparison of the sample analyteconcentrations with a set of diagnostic criteria. In one embodiment, thetypes of kidney damage identified by the multiplexed analyte panelinclude, but are not limited to glomerulonephritis, interstitialnephritis, tubular damage, vasculitis, glomerulosclerosis, and acutetubular necrosis. In another embodiment, the multiplexed analyte panelidentifies secondary kidney damaged caused by exposure to agentsincluding but not limited to therapeutic drugs, recreational drugs,medical imaging contrast agents, toxins, kidney stones, ischemia, livertransplantation, heart transplantation, lung transplantation, andhypovolemia. In yet another embodiment, the multiplexed analyte panelidentifies kidney damage caused by disease including but not limited todiabetes, hypertension, autoimmune diseases including lupus, Wegener'sgranulomatosis, Goodpasture syndrome, primary hyperoxaluria, kidneytransplant rejection, sepsis, nephritis secondary to any infection ofthe kidney, rhabdomyolysis, multiple myeloma, and prostate disease.

The following examples are included to demonstrate preferred embodimentsof the invention.

EXAMPLES

The following examples illustrate various iterations of the invention.

Example 1 Least Detectable Dose and Lower Limit of Quantitation of Assayfor Analytes Associated with Renal Disorders

To assess the least detectable doses (LDD) and lower limits ofquantitation (LLOQ) of a variety of analytes associated with renaldisorders, the following experiment was conducted. The analytes measuredwere alpha-1 microglobulin (A1M), beta-2 microglobulin (B2M), calbindin,clusterin, CTGF, cystatin C, GST-alpha, KIM-1, NGAL, osteopontin (OPN),THP, TIMP-1, TFF-3, and VEGF.

The concentrations of the analytes were measured using acapture-sandwich assay using antigen-specific antibodies. For eachanalyte, a range of standard sample dilutions ranging over about fourorders of magnitude of analyte concentration were measured using theassay in order to obtain data used to construct a standard dose responsecurve. The dynamic range for each of the analytes, defined herein as therange of analyte concentrations measured to determine its dose responsecurve, is presented below.

To perform the assay, 5 μL of a diluted mixture of capture-antibodymicrospheres were mixed with 5 μL of blocker and 10 μL of pre-dilutedstandard sample in each of the wells of a hard-bottom microtiter plate.After incubating the hard-bottom plate for 1 hour, 10 μL of biotinylateddetection antibody was added to each well, and then the hard-bottomplate was incubated for an additional hour. 10 μL of dilutedstreptavidin-phycoerythrin was added to each well and then thehard-bottom plate was incubated for another 60 minutes.

A filter-membrane microtiter plate was pre-wetted by adding 100 μL washbuffer, and then aspirated using a vacuum manifold device. The contentsof the wells of the hard-bottom plate were then transferred to thecorresponding wells of the filter-membrane plate. All wells of thehard-bottom plate were vacuum-aspirated and the contents were washedtwice with 100 μL of wash buffer. After the second wash, 100 μL of washbuffer was added to each well, and then the washed microspheres wereresuspended with thorough mixing. The plate was then analyzed using aLuminex 100 Analyzer (Luminex Corporation, Austin, Tex., USA). Doseresponse curves were constructed for each analyte by curve-fitting themedian fluorescence intensity (MFI) measured from the assays of dilutedstandard samples containing a range of analyte concentrations.

The least detectable dose (LDD) was determined by adding three standarddeviations to the average of the MFI signal measured for 20 replicatesamples of blank standard solution (i.e. standard solution containing noanalyte). The MFI signal was converted to an LDD concentration using thedose response curve and multiplied by a dilution factor of 2.

The lower limit of quantification (LLOQ), defined herein as the point atwhich the coefficient of variation (CV) for the analyte measured in thestandard samples was 30%, was determined by the analysis of themeasurements of increasingly diluted standard samples. For each analyte,the standard solution was diluted by 2 fold for 8 dilutions. At eachstage of dilution, samples were assayed in triplicate, and the CV of theanalyte concentration at each dilution was calculated and plotted as afunction of analyte concentration. The LLOQ was interpolated from thisplot and multiplied by a dilution factor of 2.

The LDD and LLOQ results for each analyte are summarized in Table 2:

TABLE 2 LDD, LLOQ, and Dynamic Range of Analyte Assay Dynamic RangeAnalyte Units LDD LLOQ minimum maximum Calbindin ng/mL 1.1 3.1 0.5162580 Clusterin ng/mL 2.4 2.3 0.676 3378 CTGF ng/mL 1.3 3.8 0.0794 400GST-alpha ng/mL 1.4 3.6 0.24 1,200 KIM-1 ng/mL 0.016 0.028 0.00478 24VEGF pg/mL 4.4 20 8.76 44,000 β-2 M μg/mL 0.012 0.018 0.0030 15 CystatinC ng/mL 2.8 3.7 0.60 3,000 NGAL ng/mL 4.1 7.8 1.2 6,000 Osteopontinng/mL 29 52 3.9 19,500 TIMP-1 ng/mL 0.71 1.1 0.073 365 A-1 M μg/mL 0.0590.29 0.042 210 THP μg/mL 0.46 0.30 0.16 800 TFF-3 μg/mL 0.06 0.097 0.060300

The results of this experiment characterized the least detectible doseand the lower limit of quantification for fourteen analytes associatedwith various renal disorders using a capture-sandwich assay.

Example 2 Precision of Assay for Analytes Associated with RenalDisorders

To assess the precision of an assay used to measure the concentration ofanalytes associated with renal disorders, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution weremeasured in triplicate during three runs using the methods described inExample 1. The percent errors for each run at each concentration arepresented in Table 3 for all of the analytes tested:

TABLE 3 Precision of Analyte Assay Average Run 1 Run 2 Run 2 Interrunconcentration Error Error Error Error Analyte (ng/mL) (%) (%) (%) (%)Calbindin 4.0 6 2 6 13 36 5 3 2 7 281 1 6 0 3 Clusterin 4.4 4 9 2 6 39 51 6 8 229 1 3 0 2 CTGF 1.2 10 17 4 14 2.5 19 19 14 14 18 7 5 13 9GST-alpha 3.9 14 7 5 10 16 13 7 10 11 42 1 16 6 8 KIM-1 0.035 2 0 5 130.32 4 5 2 8 2.9 0 5 7 4 VEGF 65 10 1 6 14 534 9 2 12 7 5,397 1 13 14 9β-2 M 0.040 6 1 8 5 0.43 2 2 0 10 6.7 6 5 11 6 Cystatin C 10.5 4 1 7 1349 0 0 3 9 424 2 6 2 5 NGAL 18.1 11 3 6 13 147 0 0 6 5 1,070 5 1 2 5Osteopontin 44 1 10 2 11 523 9 9 9 7 8,930 4 10 1 10 TIMP-1 2.2 13 6 313 26 1 1 4 14 130 1 3 1 4 A-1 M 1.7 11 7 7 14 19 4 1 8 9 45 3 5 2 4 THP9.4 3 10 11 11 15 3 7 8 6 37 4 5 0 5 TFF-3 0.3 13 3 11 12 4.2 5 8 5 71.2 3 7 0 13

The results of this experiment characterized the precision of acapture-sandwich assay for fourteen analytes associated with variousrenal disorders over a wide range of analyte concentrations. Theprecision of the assay varied between about 1% and about 15% errorwithin a given run, and between about 5% and about 15% error betweendifferent runs. The percent errors summarized in Table 2 provideinformation concerning random error to be expected in an assaymeasurement caused by variations in technicians, measuring instruments,and times of measurement.

Example 3 Linearity of Assay for Analytes Associated with RenalDisorders

To assess the linearity of an assay used to measure the concentration ofanalytes associated with renal disorders, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution weremeasured in triplicate during three runs using the methods described inExample 1. Linearity of the assay used to measure each analyte wasdetermined by measuring the concentrations of standard samples that wereserially-diluted throughout the assay range. The % recovery wascalculated as observed vs. expected concentration based on thedose-response curve. The results of the linearity analysis aresummarized in Table 4.

TABLE 4 Linearity of Analyte Assay Expected Observed Recovery AnalyteDilution concentration concentration (%) Calbindin 1:2 61 61 100 (ng/mL)1:4 30 32 106 1:8 15 17 110 Clusterin 1:2 41 41 100 (ng/mL) 1:4 21 24116 1:8 10 11 111 CTGF 1:2 1.7 1.7 100 (ng/mL) 1:4 0.84 1.0 124 1:8 0.420.51 122 GST-alpha 1:2 25 25 100 (ng/mL) 1:4 12 14 115 1:8 6.2 8.0 129KIM-1 1:2 0.87 0.87 100 (ng/mL) 1:4 0.41 0.41 101 1:8 0.21 0.19 93 VEGF1:2 2,525 2,525 100 (pg/mL) 1:4 1,263 1,340 106 1:8 631 686 109 β-2M1:100 0.63 0.63 100 (μg/mL) 1:200 0.31 0.34 106 1:400 0.16 0.17 107Cystatin C 1:100 249 249 100 (ng/mL) 1:200 125 122 102 1:400 62 56 110NGAL 1:100 1,435 1,435 100 (ng/mL) 1:200 718 775 108 1:400 359 369 103Osteopontin 1:100 6,415 6,415 100 (ng/mL) 1:200 3,208 3,275 102 1:4001,604 1,525 95 TIMP-1 1:100 35 35 100 (ng/mL) 1:200 18 18 100 1:400 8.88.8 100 A-1M 1:2000 37 37 100 (μg/mL) 1:4000 18 18 99 1:8000 9.1 9.2 99THP 1:2000 28 28 100 (μg/mL) 1:4000 14 14 96 1:8000 6.7 7.1 94 TFF-31:2000 8.8 8.8 100 (μg/mL) 1:4000 3.8 4.4 86 1:8000 1.9 2.2 86

The results of this experiment demonstrated reasonably linear responsesof the sandwich-capture assay to variations in the concentrations of theanalytes in the tested samples.

Example 4 Spike Recovery of Analytes Associated with Renal Disorders

To assess the recovery of analytes spiked into urine, serum, and plasmasamples by an assay used to measure the concentration of analytesassociated with renal disorders, the following experiment was conducted.The analytes measured were alpha-1 microglobulin (A1M), beta-2microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C, GST-alpha,KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF. For eachanalyte, three concentration levels of standard solution were spikedinto known urine, serum, and plasma samples. Prior to analysis, allurine samples were diluted 1:2000 (sample: diluent), all plasma sampleswere diluted 1:5 (sample: diluent), and all serum samples were diluted1:2000 (sample: diluent).

The concentrations of the analytes in the samples were measured usingthe methods described in Example 1. The average % recovery wascalculated as the proportion of the measurement of analyte spiked intothe urine, serum, or plasma sample (observed) to the measurement ofanalyte spiked into the standard solution (expected). The results of thespike recovery analysis are summarized in Table 5.

TABLE 5 Spike Recovery of Analyte Assay in Urine, Serum, and PlasmaSamples Recovery in Recovery in Recovery in Spike Urine Serum PlasmaAnalyte Concentration Sample (%) Sample (%) Sample (%) Calbindin 66 7682 83 (ng/mL) 35 91 77 71 18 80 82 73 average 82 80 76 Clusterin 80 7273 75 (ng/mL) 37 70 66 72 20 90 73 70 average 77 70 72 CTGF 8.4 91 80 79(ng/mL) 4.6 114 69 78 2.4 76 80 69 average 94 77 75 GST-alpha 27 75 8480 (ng/mL) 15 90 75 81 7.1 82 84 72 average 83 81 78 KIM-1 0.63 87 80 83(ng/mL) .029 119 74 80 0.14 117 80 78 average 107 78 80 VEGF 584 88 8482 (pg/mL) 287 101 77 86 123 107 84 77 average 99 82 82 β-2M 0.97 117 9898 (μg/mL) 0.50 124 119 119 0.24 104 107 107 average 115 108 105Cystatin C 183 138 80 103 (ng/mL) 90 136 97 103 40 120 97 118 average131 91 108 NGAL 426 120 105 111 (ng/mL) 213 124 114 112 103 90 99 113average 111 106 112 Osteopontin 1,245 204 124 68 (ng/mL) 636 153 112 69302 66 103 67 average 108 113 68 TIMP-1 25 98 97 113 (ng/mL) 12 114 89103 5.7 94 99 113 average 102 95 110 A-1M 0.0028 100 101 79 (μg/mL)0.0012 125 80 81 0.00060 118 101 82 Average 114 94 81 THP 0.0096 126 10890 (μg/mL) 0.0047 131 93 91 0.0026 112 114 83 average 123 105 88 TFF-30.0038 105 114 97 (μg/mL) 0.0019 109 104 95 0.0010 102 118 93 average105 112 95

The results of this experiment demonstrated that the sandwich-type assayis reasonably sensitive to the presence of all analytes measured,whether the analytes were measured in standard samples, urine samples,plasma samples, or serum samples.

Example 5 Matrix Interferences of Analytes Associated with RenalDisorders

To assess the matrix interference of hemoglobin, bilirubin, andtriglycerides spiked into standard samples, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.For each analyte, three concentration levels of standard solution werespiked into known urine, serum, and plasma samples. Matrix interferencewas assessed by spiking hemoglobin, bilirubin, and triglyceride intostandard analyte samples and measuring analyte concentrations using themethods described in Example 1. A % recovery was determined bycalculating the ratio of the analyte concentration measured from thespiked sample (observed) divided by the analyte concentration measuredform the standard sample (expected). The results of the matrixinterference analysis are summarized in Table 6.

TABLE 6 Matrix Interference of Hemoglobin, Bilirubin, and Triglycerideon the Measurement of Analytes Matrix Compound Maximum Spiked into SpikeOverall Analyte Sample Concentration Recovery (%) Calbindin Hemoglobin500 110 (mg/mL) Bilirubin 20 98 Triglyceride 500 117 ClusterinHemoglobin 500 125 (mg/mL) Bilirubin 20 110 Triglyceride 500 85 CTGFHemoglobin 500 91 (mg/mL) Bilirubin 20 88 Triglyceride 500 84 GST-alphaHemoglobin 500 100 (mg/mL) Bilirubin 20 96 Triglyceride 500 96 KIM-1Hemoglobin 500 108 (mg/mL) Bilirubin 20 117 Triglyceride 500 84 VEGFHemoglobin 500 112 (mg/mL) Bilirubin 20 85 Triglyceride 500 114 β-2MHemoglobin 500 84 (μg/mL) Bilirubin 20 75 Triglyceride 500 104 CystatinC Hemoglobin 500 91 (ng/mL) Bilirubin 20 102 Triglyceride 500 124 NGALHemoglobin 500 99 (ng/mL) Bilirubin 20 92 Triglyceride 500 106Osteopontin Hemoglobin 500 83 (ng/mL) Bilirubin 20 86 Triglyceride 500106 TIMP-1 Hemoglobin 500 87 (ng/mL) Bilirubin 20 86 Triglyceride 500 93A-1M Hemoglobin 500 103 (μg/mL) Bilirubin 20 110 Triglyceride 500 112THP Hemoglobin 500 108 (μg/mL) Bilirubin 20 101 Triglyceride 500 121TFF-3 Hemoglobin 500 101 (μg/mL) Bilirubin 20 101 Triglyceride 500 110

The results of this experiment demonstrated that hemoglobin, bilirubin,and triglycerides, three common compounds found in urine, plasma, andserum samples, did not significantly degrade the ability of thesandwich-capture assay to detect any of the analytes tested.

Example 6 Sample Stability of Analytes Associated with Renal Disorders

To assess the ability of analytes spiked into urine, serum, and plasmasamples to tolerate freeze-thaw cycles, the following experiment wasconducted. The analytes measured were alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.Each analyte was spiked into known urine, serum, and plasma samples at aknown analyte concentration. The concentrations of the analytes in thesamples were measured using the methods described in Example 1 after theinitial addition of the analyte, and after one, two and three cycles offreezing and thawing. In addition, analyte concentrations in urine,serum and plasma samples were measured immediately after the addition ofthe analyte to the samples as well as after storage at room temperaturefor two hours and four hours, and after storage at 4° C. for 2 hours,four hours, and 24 hours.

The results of the freeze-thaw stability analysis are summarized inTable 7. The % recovery of each analyte was calculated as a percentageof the analyte measured in the sample prior to any freeze-thaw cycles.

TABLE 7 Freeze-Thaw Stability of the Analytes in Urine, Serum, andPlasma Period Urine Sample Serum Sample Plasma Sample and Concen-Recovery Concen- Recovery Concen- Recovery Analyte Temp tration (%)tration (%) tration (%) Calbindin Control 212 100 31 100 43 100 (ng/mL)1X 221 104 30 96 41 94 2X 203 96 30 99 39 92 3X 234 110 30 97 40 93Clusterin 0 315 100 232 100 187 100 (ng/mL) 1X 329 104 227 98 177 95 2X341 108 240 103 175 94 3X 379 120 248 107 183 98 CTGF 0 6.7 100 1.5 1001.2 100 (ng/mL) 1X 7.5 112 1.3 82 1.2 94 2X 6.8 101 1.4 90 1.2 100 3X7.7 115 1.2 73 1.3 107 GST- 0 12 100 23 100 11 100 alpha 1X 13 104 24105 11 101 (ng/mL) 2X 14 116 21 92 11 97 3X 14 111 23 100 12 108 KIM-1 01.7 100 0.24 100 0.24 100 (ng/mL) 1X 1.7 99 0.24 102 0.22 91 2X 1.7 990.22 94 0.19 78 3X 1.8 107 0.23 97 0.22 93 VEGF 0 1,530 100 1,245 100674 100 (pg/mL) 1X 1,575 103 1,205 97 652 97 2X 1,570 103 1,140 92 61291 3X 1,700 111 1,185 95 670 99 β-2 M 0 0.0070 100 1.2 100 15 100(μg/mL) 1X 0.0073 104 1.1 93 14 109 2X 0.0076 108 1.2 103 15 104 3X0.0076 108 1.1 97 13 116 Cystatin 0 1,240 100 1,330 100 519 100 C 1X1,280 103 1,470 111 584 113 (ng/mL) 2X 1,410 114 1,370 103 730 141 3X1,420 115 1,380 104 589 113 NGAL 0 45 100 245 100 84 100 (ng/mL) 1X 46102 179 114 94 112 2X 47 104 276 113 91 108 3X 47 104 278 113 91 109Osteo- 0 38 100 1.7 100 5.0 100 pontin 1X 42 110 1.8 102 5.5 110 (ng/mL)2X 42 108 1.5 87 5.5 109 3X 42 110 1.3 77 5.4 107 TIMP-1 0 266 100 220100 70 100 (ng/mL) 1X 265 100 220 10 75 108 2X 255 96 215 98 77 110 3X295 111 228 104 76 109 A-1 M 0 14 100 26 100 4.5 100 (μg/mL) 1X 13 92 2596 4.2 94 2X 15 107 25 96 4.3 97 3X 16 116 23 88 4.0 90 THP 0 4.6 100 31100 9.2 100 (μg/mL) 1X 4.4 96 31 98 8.8 95 2X 5.0 110 31 100 9.2 100 3X5.2 114 27 85 9.1 99 TFF-3 0 4.6 100 24 100 22 100 (μg/mL) 1X 4.4 96 2398 22 103 2X 5.0 110 24 103 22 101 3X 5.2 114 19 82 22 102

The results of the short-term stability assessment are summarized inTable 8. The % recovery of each analyte was calculated as a percentageof the analyte measured in the sample prior to any short-term storage.

TABLE 8 Short-Term Stability of Analytes in Urine, Serum, and PlasmaStorage Urine Sample Serum Sample Plasma Sample Time/ Sample RecoverySample Recovery Sample Recovery Analyte Temp Conc. (%) Conc. (%) Conc.(%) Cal- Control 226 100 33 100 7 100 bindin 2 hr/room 242 107 30 90 6.390 (ng/mL) temp 2 hr. @ 228 101 29 89 6.5 93 4° C. 4 hr @ 240 106 28 845.6 79 room temp 4 hr. @ 202 89 29 86 5.5 79 4° C. 24 hr. @ 199 88 26 787.1 101 4° C. Clus- Control 185 100 224 100 171 100 terin 2 hr @ 173 94237 106 180 105 (ng/mL) room temp 2 hr. @ 146 79 225 100 171 100 4° C. 4hr @ 166 89 214 96 160 94 room temp 4 hr. @ 157 85 198 88 143 84 4° C.24 hr. @ 185 100 207 92 162 94 4° C. CTGF Control 1.9 100 8.8 100 1.2100 (ng/mL) 2 hr @ 1.9 99 6.7 76 1 83 room temp 2 hr. @ 1.8 96 8.1 921.1 89 4° C. 4 hr @ 2.1 113 5.6 64 1 84 room temp 4 hr. @ 1.7 91 6.4 740.9 78 4° C. 24 hr. @ 2.2 116 5.9 68 1.1 89 4° C. GST- Control 14 100 21100 11 100 alpha 2 hr @ 11 75 23 107 11 103 (ng/mL) room temp 2 hr. @ 1393 22 104 9.4 90 4° C. 4 hr @ 11 79 21 100 11 109 room temp 4 hr. @ 1289 21 98 11 100 4° C. 24 hr. @ 13 90 22 103 14 129 4° C. KIM-1 Control1.5 100 0.23 100 0.24 100 (ng/mL) 2 hr @ 1.2 78 0.2 86 0.22 90 room temp2 hr. @ 1.6 106 0.23 98 0.21 85 4° C. 4 hr @ 1.3 84 0.19 82 0.2 81 roomtemp 4 hr. @ 1.4 90 0.22 93 0.19 80 4° C. 24 hr. @ 1.1 76 0.18 76 0.2394 4° C. VEGF Control 851 100 1215 100 670 100 (pg/mL) 2 hr @ 793 931055 87 622 93 room temp 2 hr. @ 700 82 1065 88 629 94 4° C. 4 hr @ 70483 1007 83 566 84 room temp 4 hr. @ 618 73 1135 93 544 81 4° C. 24 hr. @653 77 1130 93 589 88 4° C. β-2 M Control 0.064 100 2.6 100 1.2 100(μg/mL) 2 hr @ 0.062 97 2.4 92 1.1 93 room temp 2 hr. @ 0.058 91 2.2 851.2 94 4° C. 4 hr @ 0.064 101 2.2 83 1.2 94 room temp 4 hr. @ 0.057 902.2 85 1.2 98 4° C. 24 hr. @ 0.06 94 2.5 97 1.3 103 4° C. Cys- Control52 100 819 100 476 100 tatin 2 hr @ 50 96 837 102 466 98 C room temp(ng/mL) 2 hr. @ 44 84 884 108 547 115 4° C. 4 hr @ 49 93 829 101 498 105room temp 4 hr. @ 46 88 883 108 513 108 4° C. 24 hr. @ 51 97 767 94 47199 4° C. NGAL Control 857 100 302 100 93 100 (ng/mL) 2 hr @ 888 104 28795 96 104 room temp 2 hr. @ 923 108 275 91 92 100 4° C. 4 hr @ 861 101269 89 88 95 room temp 4 hr. @ 842 98 283 94 94 101 4° C. 24 hr. @ 960112 245 81 88 95 4° C. Osteo- Control 2243 100 6.4 100 5.2 100 pontin 2hr @ 2240 100 6.8 107 5.9 114 (ng/mL) room temp 2 hr. @ 2140 95 6.4 1016.2 120 4° C. 4 hr @ 2227 99 6.9 108 5.8 111 room temp 4 hr. @ 2120 957.7 120 5.2 101 4° C. 24 hr. @ 2253 100 6.5 101 6 116 4° C. TIMP-1Control 17 100 349 100 72 100 (ng/mL) 2 hr @ 17 98 311 89 70 98 roomtemp 2 hr. @ 16 94 311 89 68 95 4° C. 4 hr @ 17 97 306 88 68 95 roomtemp 4 hr. @ 16 93 329 94 74 103 4° C. 24 hr. @ 18 105 349 100 72 100 4°C. A-1 M Control 3.6 100 2.2 100 1 100 (μg/mL) 2 hr @ 3.5 95 2 92 1 105room temp 2 hr. @ 3.4 92 2.1 97 0.99 99 4° C. 4 hr @ 3.2 88 2.2 101 0.9996 room temp 4 hr. @ 3 82 2.2 99 0.97 98 4° C. 24 hr. @ 3 83 2.2 100 1101 4° C. THP Control 1.2 100 34 100 2.1 100 (μg/mL) 2 hr @ 1.2 99 34 992 99 room temp 2 hr. @ 1.1 90 34 100 2 98 4° C. 4 hr @ 1.1 88 27 80 2 99room temp 4 hr. @ 0.95 79 33 97 2 95 4° C. 24 hr. @ 0.91 76 33 98 2.4116 4° C. TFF-3 Control 1230 100 188 100 2240 100 (μg/mL) 2 hr @ 1215 99179 95 2200 98 room temp 2 hr. @ 1200 98 195 104 2263 101 4° C. 4 hr @1160 94 224 119 2097 94 room temp 4 hr. @ 1020 83 199 106 2317 103 4° C.24 hr. @ 1030 84 229 122 1940 87 4° C.

The results of this experiment demonstrated that the analytes associatedwith renal disorders tested were suitably stable over severalfreeze/thaw cycles, and up to 24 hrs. of storage at a temperature of 4°C.

Example 8 Diagnosis of Renal Damage Using Detection of Analytes in HumanUrine Samples

To assess the effectiveness of a human kidney toxicity panel to detectrenal damage due to disease states, the following experiment wasconducted. Urine samples were obtained from healthy control patients(n=5), renal cancer patients (n=4) and “other” cancer patients (n=8)afflicted with lung cancer, pancreatic cancer, liver cancer, or coloncancer. All urine samples were diluted as described in Example 4 andsubjected to a sandwich-capture assay as described in Example 1. Urineconcentrations of analytes included in a human kidney toxicity panelwere measured by the assay, including alpha-1 microglobulin (A1M),beta-2 microglobulin (B2M), calbindin, clusterin, CTGF, cystatin C,GST-alpha, KIM-1, NGAL, osteopontin (OPN), THP, TIMP-1, TFF-3, and VEGF.

FIG. 1 summarizes the urine concentrations of those analytes thatdiffered significantly from control urine concentrations. The urineconcentrations of A1M, NGAL, and THP were slightly elevated for therenal cancer patient group and more significantly elevated for the“other” cancer patient group. Urine B2M concentrations appeared to beelevated for both the renal cancer and “other” cancer patient groups,although the BRM concentrations exhibited more variability than theother analyte concentrations shown in FIG. 1.

The results of this experiment demonstrated that panels of analytesdetected in urine samples were capable of identifying patients havingrenal damage resulting from renal cancer and other cancers.

Example 9 Analysis of Kidney Biomarkers in Plasma and Urine fromPatients with Renal Injury

A screen for potential protein biomarkers in relation to kidneytoxicity/damage was performed using a panel of biomarkers, in a set ofurine and plasma samples from patients with documented renal damage. Theinvestigated patient groups included diabetic nephropathy (DN),obstructive uropathy (OU), analgesic abuse (AA) and glomerulonephritis(GN) along with age, gender and BMI matched control groups. Multiplexedimmunoassays were applied in order to quantify the following proteinanalytes: Alpha-1 Microglobulin (α1M), KIM-1, Microalbumin,Beta-2-Microglobulin (β2M), Calbindin, Clusterin, CystatinC,TreFoilFactor-3 (TFF-3), CTGF, GST-alpha, VEGF, Calbindin, Osteopontin,Tamm-HorsfallProtein (THP), TIMP-1 and NGAL.

Li-Heparin plasma and mid-stream spot urine samples were collected fromfour different patient groups. Samples were also collected from age,gender and BMI matched control subjects. 20 subjects were included ineach group resulting in a total number of 160 urine and plasma samples.All samples were stored at −80° C. before use. Glomerular filtrationrate for all samples was estimated using two different estimations(Modification of Diet in Renal Disease or MDRD, and the Chronic KidneyDisease Epidemiology Collaboration or CKD-EPI) to outline the eGFR(estimated glomerular filtration rate) distribution within each patientgroup (FIG. 2). Protein analytes were quantified in human plasma andurine using multiplexed immunoassays in the Luminex xMAP™ platform. Themicrosphere-based multiplex immunoassays consist of antigen-specificantibodies and optimized reagents in a capture-sandwich format. Outputdata was given as g/ml calculated from internal standard curves. Becauseurine creatinine (uCr) correlates with renal filtration rate, dataanalysis was performed without correction for uCr. Univariate andmultivariate data analysis was performed comparing all case vs. controlsamples as well as cases vs. control samples for the various diseasegroups.

The majority of the measured proteins showed a correlation to eGFR.Measured variables were correlated to eGFR using Pearson's correlationscoefficient, and samples from healthy controls and all disease groupswere included in the analysis. 11 and 7 proteins displayed P-valuesbelow 0.05 for plasma and urine (Table 9) respectively.

TABLE 9 Correlation analysis of eGFR and variables for all case samplesURINE PLASMA Variable Pearson's r P-Value Variable Pearson's r P-ValueAlpha-1- −0.08   0.3 Alpha-1- −0.33

Microglobulin Microglobulin Beta-2- −0.23   0.003 Beta-2- −0.39

Microglobulin Microglobulin Calbindin −0.16   0.04 Calbindin −0.18 <0.02Clusterin −0.07   0.4 Clusterin −0.51

CTGF −0.08   0.3 CTGF −0.05   0.5 Creatinine −0.32

Cystatin-C −0.42 <0.0001 Cystatin-C −0.24   0.002 GST-alpha −0.12   0.1GST-alpha −0.11   0.2 KIM-1 −0.17   0.03 KIM-1 −0.08   0.3 NGAL −0.28<0.001 Microalbumin_UR −0.17   0.03 Osteopontin −0.33

NGAL −0.15   0.07 THP −0.31

Osteopontin −0.19   0.02 TIMP-1 −0.28 <0.001 THP −0.05   0.6 TFF3 −0.38

TIMP-1 −0.19   0.01 VEGF −0.14   0.08 TFF2 −0.09   0.3 VEGF −0.07   0.4P values <0.0001 are shown in bold italics P values <0.005 are shown inbold P values <0.05 are shown in italics

For the various disease groups, univariate statistical analysis revealedthat in a direct comparison (T-test) between cases and controls, anumber of proteins were differentially expressed in both urine andplasma (Table 10 and FIG. 3). In particular, clusterin showed a markeddifferential pattern in plasma.

TABLE 10 Differentially regulated proteins by univariate statisticalanalysis Group Matrix Protein p-value AA Urine Calbindin 0.016 AA UrineNGAL 0.04 AA Urine Osteopontin 0.005 AA Urine Creatinine 0.001 AA PlasmaCalbindin 0.05 AA Plasma Clusterin 0.003 AA Plasma KIM-1 0.03 AA PlasmaTHP 0.001 AA Plasma TIMP-1 0.02 DN Urine Creatinine 0.04 DN PlasmaClusterin 0.006 DN Plasma KIM-1 0.01 GN Urine Creatinine 0.004 GN UrineMicroalbumin 0.0003 GN Urine NGAL 0.05 GN Urine Osteopontin 0.05 GNUrine TFF3 0.03 GN Plasma Alpha 1 Microglobulin 0.002 GN Plasma Beta 2Microglobulin 0.03 GN Plasma Clusterin 0.00 GN Plasma Cystatin C 0.01 GNPlasma KIM-1 0.003 GN Plasma NGAL 0.03 GN Plasma THP 0.001 GN PlasmaTIMP-1 0.003 GN Plasma TFF3 0.01 GN Plasma VEGF 0.02 OU Urine Clusterin0.02 OU Urine Microalbumin 0.007 OU Plasma Clusterin 0.00

Application of multivariate analysis yielded statistical models thatpredicted disease from control samples (plasma results are shown in FIG.4).

In conclusion, these results form a valuable base for further studies onthese biomarkers in urine and plasma both regarding baseline levels innormal populations and regarding the differential expression of theanalytes in various disease groups. Using this panel of analytes, errorrates from adaboosting and/or random forest were low enough (<10%) toallow a prediction model to differentiate between control and diseasepatient samples. Several of the analytes showed a greater correlation toeGFR in plasma than in urine.

Example 10 Statistical Analysis of Kidney Biomarkers in Plasma and Urinefrom Patients with Renal Injury

Urine and plasma samples were taken from 80 normal control groupsubjects and 20 subjects from each of four disorders: analgesic abuse,diabetic nephropathy, glomerulonephritis, and obstructive uropathy. Thesamples were analyzed for the quantity and presence of 16 differentproteins (alpha-1 microglobulin (α1M), beta-2 microglobulin (β2M),calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1,microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, and VEGF) asdescribed in Example 1 above. The goal was to determine the analytesthat distinguish between a normal sample and a diseased sample, a normalsample and an obstructive uropathy (OU) sample, and finally, anglomerulonephritis sample from the other disease samples (diabeticnephropathy (DN), analgesic abuse (AA), and glomerulonephritis (GN)).

From the above protein analysis data, bootstrap analysis was used toestimate the future performance of several classification algorithms.For each bootstrap run, training data and testing data was randomlygenerated. Then, the following algorithms were applied on the trainingdata to generate models and then apply the models to the testing data tomake predictions: automated Matthew's classification algorithm,classification and regression tree (CART), conditional inference tree,bagging, random forest, boosting, logistic regression, SVM, and Lasso.The accuracy rate and ROC areas were recorded for each method on theprediction of the testing data. The above was repeated 100 times. Themean and the standard deviation of the accuracy rates and of the ROCareas were calculated.

The mean error rates and AUROC were calculated from urine and AUROC wascalculated from plasma for 100 runs of the above method for each of thefollowing comparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 5, Table11), AA vs. normal (FIG. 7, Table 13), DN vs. AA (FIG. 9, Table 15, AAvs. GN (FIG. 11, Table 17), and AA vs. OU (FIG. 13, Table 19).

The average relative importance of 16 different analytes (alpha-1microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF,creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL,osteopontin, THP, TIMP-1, TFF-3, and VEGF) and 4 different clinicalvariables (weight, BMI, age, and gender) from 100 runs were analyzedwith two different statistical methods—random forest (plasma and urinesamples) and boosting (urine samples)—for each of the followingcomparisons: disease (AA+GN+OU+DN) vs. normal (FIG. 6, Table 12), AA vs.normal (FIG. 8, Table 14), DN vs. AA (FIG. 10, Table 16), AA vs. GN(FIG. 12, Table 18), and AA vs. OU (FIG. 14, Table 20).

TABLE 11 Disease v. Normal Standard Mean deviation method AUROC AUROCrandom 0.931 0.039 forest bagging 0.919 0.045 svm 0.915 0.032 boosting0.911 0.06 lasso 0.897 0.044 logistic 0.891 0.041 regression ctree 0.8470.046 cart 0.842 0.032 matt 0.83 0.023

TABLE 12 Disease v. Normal relative analyte importance Creatinine 11.606Kidney_Injury_M 8.486 Tamm_Horsfall_P 8.191 Total_Protein 6.928Osteopontin 6.798 Neutrophil_Gela 6.784 Tissue_Inhibito 6.765Vascular_Endoth 6.716 Trefoil_Factor_(—) 6.703 Cystatin_C 6.482Alpha_1_Microgl 6.418 Beta_2_Microglo 6.228 Glutathione_S_T 6.053clusterin 5.842

TABLE 13 AA v. NL Standard deviation Mean of method AUROC AUROC cart 1 0bagging 1 0 boosting 1 0 lasso 0.998 0.008 ctree 0.998 0.015 random0.997 0.012 forest svm 0.977 0.033 logistic 0.933 0.092 regression matt0.873 0.112

TABLE 14 AA v. NL Relative analyte importance Creatinine 17.800Tissue_Inhibito 9.953 Total_Protein 8.837 Tamm_Horsfall_P 7.379Cystatin_C 6.237 Kidney_Injury_M 6.174 Beta_2_Microglo 5.915Neutrophil_Gela 5.761 Alpha_1_Microgl 5.742 Trefoil_Factor_(—) 5.736Osteopontin 5.561 Vascular_Endoth 5.338 clusterin 4.892 Glutathione_S_T4.675

TABLE 15 AA v. DN Standard Mean deviation method AUROC AUROC lasso 0.9990.008 random 0.989 0.021 forest svm 0.988 0.039 boosting 0.988 0.022bagging 0.972 0.036 logistic 0.969 0.057 regression cart 0.93 0.055ctree 0.929 0.063 matt 0.862 0.12

TABLE 16 AA v. DN Relative analyte importance Creatinine 17.57Total_Protein 10.90 Tissue_Inhibito 8.77 clusterin 6.89 Glutathione_S_T6.24 Alpha_1_Microgl 6.15 Beta_2_Microglo 6.06 Cystatin_C 5.99Trefoil_Factor_(—) 5.88 Kidney_Injury_M 5.49 Vascular_Endoth 5.38Tamm_Horsfall_P 5.33 Osteopontin 4.86 Neutrophil_Gela 4.47

TABLE 17 AA v. GN Standard deviation Mean of method AUROC AUROC svm0.689 0.11 boosting 0.675 0.102 bagging 0.674 0.106 random 0.66 0.096forest matt 0.631 0.085 cart 0.626 0.089 logistic 0.614 0.091 regressionlasso 0.606 0.102 ctree 0.53 0.061

TABLE 18 AA v. GN Relative analyte importance Creatinine 10.780Alpha_1_Microgl 8.847 Kidney_Injury_M 8.604 clusterin 8.109Total_Protein 7.679 Glutathione_S_T 7.493 Neutrophil_Gela 6.721Vascular_Endoth 6.461 Cystatin_C 6.444 Beta_2_Microglo 6.261Trefoil_Factor_(—) 6.184 Tamm_Horsfall_P 5.872 Tissue_Inhibito 5.690Osteopontin 4.855

TABLE 19 AA v. OU Standard deviation Mean of method AUROC AUROC random0.814 0.11 forest bagging 0.792 0.115 svm 0.788 0.112 lasso 0.786 0.118boosting 0.757 0.117 matt 0.687 0.111 logistic 0.683 0.116 regressioncart 0.665 0.097 ctree 0.659 0.118

TABLE 20 AA v. OU Relative analyte importance Total_Protein 11.502Tissue_Inhibito 9.736 Cystatin_C 9.161 Alpha_1_Microgl 8.637Trefoil_Factor_(—) 7.329 Osteopontin 7.326 Beta_2_Microglo 6.978Neutrophil_Gela 6.577 Glutathione_S_T 6.100 Tamm_Horsfall_P 6.066Kidney_Injury_M 6.038 Vascular_Endoth 5.946 clusterin 4.751 Creatinine3.854

VI. Automated Method for Diagnosing, Monitoring, or Determining a RenalDisorder

FIG. 15 is a block diagram of an exemplary computing environment 1500for diagnosing, monitoring, and/or determining a renal disorder in amammal. The computing environment 1500 includes sample input device1502, a renal disorder diagnostics system (RDSS) 1504, and a data source1506.

According to one aspect, sample input device 1502 is a computer orprocessing device 1508, such as a personal computer, a server computer,or a mobile processing device. The computer 1508 may include a displaysuch as a computer monitor, for viewing data, and an input device, suchas a keyboard or a pointing device (e.g., a mouse, trackball, pen, touchpad, or other device), for entering data. The computer 1508 is used by auser to enter analyte concentrations of a test sample for processing bythe RDSS 1504. For example, the user uses the keyboard to interact withan analyte concentration entry form (not shown) on the display to entertest sample analyte data that includes, for example, three or moreanalyte concentrations.

In another embodiment, the test sample analyte concentrations arecollected and then transmitted to the RDSS 1504 via an analytemeasurement/sensor device 1510 (e.g., multiplexed immunoassay device)that measures the sample analyte concentration. The analytemeasurement/sensor device 1510 communicates the measured sample analyteconcentrations data to the RDSS 1504 via a data cable, infrared signal,wireless connection, or other methods of data transmission known in theart.

The RDDS 1504 executes a renal disorder determining application 1512 inresponse to test sample analyte concentration data received from thereceived from the sample input device 102. The renal disorderdetermining application (RDDA) 1512 analyzes the analyte concentrationdata for the test sample and determines whether the received analyteconcentration data is indicative of renal disorder and, if so, a type ofrenal disorder. The renal disorder determining application 1512 thendisplays whether the result of the analysis is positive or negative fora renal disorder and, if applicable, the type of renal disorder.

According to one aspect, the RDDS 1904 retrieves concentration thresholddata and/or disorder threshold data from the data source 1506 todetermine whether the received analyte concentration data is indicativeof one or more renal disorders. The data source 1506 is, for example, acomputer system, a database, or another data system that stores data,electronic documents, records, other documents, and/or other data. Thedata source 1506 may include memory and one or more processors orprocessing systems to receive, process, and transmit communications andstore and retrieve data.

According to one aspect, the data source 1506 includes a diagnosticanalytic concentrations database 1514 that stores normal ranges ofbiomarker analytes for human plasma, serum, and urine, such describedabove in connection with Table 1. The entries of the diagnostic analyticconcentrations database 1514 may also include additional minimumdiagnostic concentrations to further define diagnostic criteriaincluding but not limited to minimum diagnostic concentrations foradditional types of bodily fluids, additional types of mammals, andseverities of a particular disorder. As described above, if the measuredconcentration for a particular analyte of a sample of plasma exceeds thehigh value in Table 1, then the measured concentration of thatparticular may be indicative of a renal disorder or disease in thesubject from with the test sample was collected.

According to one aspect, the disorder database 1516 includes variousdata tables index by disorder or disease type. Each data tablecorresponds to a specific disorder/disease type and identifies a list ofminimum diagnostic concentrations that are indicative of that particulardisease. For example, diabetic nephropathy data table indicates bysample type (i.e., plasma, urine, serum) the minimum concentrationrequired, if any, for each of sixteen analyte biomarkers described abovein connection with Table 1.

Although, the data source is illustrated in FIG. 15 as being integratedwith the RDDS 1504, it is contemplated that in other aspects the datasource 1506 may be separate and/or remote from the RDDS 1504. Accordingto one such aspect, the RDDS 1504 communicates with the data source 1506over a communication network, such as the Internet, an intranet, anEthernet network, a wireline network, a wireless network, and/or anothercommunication network, to identify relevant images, electronicdocuments, records, other documents, and/or other data to retrieve fromthe data source 1506. In another aspect, the sample input device 1502communicates with the RDDS 1904 through the communication network. Instill another aspect, the RDDS 1504 communicates with the data source1506 through a direct connection.

FIG. 16 is a block diagram that depicts an exemplary RDDS 1504.According to one aspect, the RDDS 1504 includes a processing system 1602that executes the RDDA 1512 to determine whether the received analyteconcentration data is indicative of renal disorder and, if so, the typeof renal disorder. The processing system 1602 includes memory and one ormore processors, and the processing system 1602 can reside on a computeror other processing system. In this aspect, the data source 1506 is notshown and is, for example, located remotely from the RDDS 1504.

The RDDA 1512 includes instructions or modules that are executable bythe processing system 1602 to manage the retrieval of renal disorderdiagnostic data, including a record, from the data source 1506. The RDDS1504 includes computer readable media 1604 configured with the RDDA1512.

Computer readable medium (CRM) 1604 may include volatile media,nonvolatile media, removable media, non-removable media, and/or anotheravailable medium that can be accessed by the RDDS 1504. By way ofexample and not limitation, computer readable medium 1604 comprisescomputer storage media and communication media. Computer storage mediaincludes memory, volatile media, nonvolatile media, removable media,and/or non-removable media implemented in a method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data. Communication media mayembody computer readable instructions, data structures, program modules,or other data and include an information delivery media or system.

An analyte input module 1606 receives three or more sample analyteconcentrations that include the biomarker analytes. In one embodiment,the sample analyte concentrations are entered as input by a user of thecomputer 1508. In another embodiment, the sample analyte concentrationsare received directly from analyte measure/sensor device 1510, such as amultiplexed immunoassay device.

In another embodiment, the analyte input module 1606 receives sampleanalyte concentrations for at least six biomarker analytes. In oneexample, the at least six biomarker analytes include alpha 1microglobulin, cystatin C, KIM-1, Tamm-Horsfall, Beta 2-microglobulin,and TIMP-1.

In another embodiment, the analyte input module 1606 receives sampleanalyte concentrations for sixteen biomarker analytes. In one example,the sixteen biomarker analytes include the analyte types shown in Table1.

A comparison module 1608 compares each analyte concentration of a samplereceived from the analyte input module 1606 to a corresponding analyteentry in the diagnostic analyte database to determine if one or moreconcentrations for a particular analyte of the sample are exceed theminimum diagnostic value for that particular analyte. For example,referring briefly to Table 1, if the sample concentrations are obtainedfrom plasma and the particular analyte is calbindin, the comparisonmodule compares the measured calbindin analyte concentration to thesample to the corresponding high concentration value for plasma todetermine if it is greater than about 5 ng/ml. A measured calbindinanalyte concentration less than about 5 ng/ml indicates is notindicative of renal disorder. In contrast, a measured calbindin analyteconcentration that is greater than about 5 ng/ml is indicative of arenal disorder.

An analysis module 1610 determines a most likely renal disorder as afunction of the particular measured analyte concentrations identified asindicative of a renal disorder by the comparison module. For example,the analysis module 1610 compares the particular measured analyteconcentrations to entries in the disorder tables stored in the renaldisorder database 1516 to identify the most likely type renal disorder.Each disorder table includes, for example, the minimum concentrations orthreshold concentrations for each of the sixteen analytes types shown inTable 1 that are associated with the diagnosis of a particular renaldisorder or disease. It is also contemplated that the analyte typeslisted in a disorder table for particular renal disorder or disease maybe different from the analyte types listed in another disorder table fora different renal disorder or disease.

In one embodiment, the most likely renal disorder is the particularrenal disorder type in the disorder database 1516 having the mostminimum diagnostic concentrations that are less than the correspondingsample analyte concentrations. In other words, the most likely disorderis identified from the disorder table that includes the most thresholdconcentrations that are exceeded by the sample analyte concentrations.For example, consider that five of the sample analyte concentrationsexceed the minimum threshold concentrations for corresponding analytesin the disorder table for a first renal disorder, such as analgesicabuse. Also, consider that four of the sample analyte concentrationsexceed the minimum threshold concentrations for corresponding analytesin a disorder table for a second renal disorder, such as obstructiveuropathy. In this example, the most likely renal disorder is analgesicabuse.

In one embodiment, the most likely renal disorder is the particularrenal disorder type in the disorder database 1516 having the mostminimum diagnostic concentrations that are less than the correspondingsample analyte concentrations. In other words, the most likely disorderis identified from the disorder table that includes the most thresholdconcentrations that are exceeded by the sample analyte concentrations.For example, consider that five of the sample analyte concentrationsexceed the minimum threshold concentrations for corresponding analytesin a disorder table for a first renal disorder, such as analgesic abuse.Also, consider that four of the sample analyte concentrations exceed theminimum threshold concentrations for corresponding analytes in adisorder table for a second renal disorder, such as obstructiveuropathy. In this example, the most likely renal disorder is analgesicabuse.

In another embodiment, the most likely renal disorder is the particularrenal disorder from the database entry having minimum diagnosticconcentrations that are all less than the corresponding sample analyteconcentrations.

In yet other embodiments, the analysis module 1610 combines the sampleanalyte concentrations algebraically to calculate a combined sampleanalyte concentration that is compared to a combined minimum diagnosticconcentration calculated from the corresponding minimum diagnosticcriteria using the same algebraic operations. See Table A for examplecombinations. Other combinations of sample analyte concentrations fromwithin the same test sample, or combinations of sample analyteconcentrations from two or more different test samples containing two ormore different bodily fluids may be used to determine a particular renaldisorder in still other embodiments.

An output module 1612 generates a display of analyte types andcorresponding concentrations for each of the measured analytesidentified as indicative of a renal disorder by the comparison module.The output module 1612 also generates a display of the most likely renaldisorder determined by the analysis module 1610.

FIG. 17 illustrates a method for diagnosing, monitoring, or determininga renal disorder in a mammal in accordance with an aspect of the RDDS1504. At 1702, analyte concentrations read by an assay device or definedvia user input at a computer are communicated to the renal disorderdetermining application 1512. At 1704, the sample analyte concentrationsare transferred to the RDSS 1504 for processing. The concentration ofeach analyte type in the sample is compared to a corresponding thresholdanalyte concentration in a diagnostic analyte database at 1706. Asdescribed above, the threshold analyte concentrations in the diagnosticanalyte database correspond to analyte concentration for various sampletypes that have been previous determined to be indicative of one or morerenal disorders or diseases. If none of the analyte concentrations forthe sample are determined to be greater than the corresponding thresholdanalyte concentrations at 1708. The one or more of the analyteconcentrations and/or a message indicating the concentrations are withinnormal range is generated for display via the computer at 1710.

If one or more of the analyte concentrations for the sample aredetermined to be greater than the corresponding threshold analyteconcentrations at 1708, the one or more analyte concentrations are thencompared to disorder threshold analyte concentrations in a disorderdatabase at 1712. The disorder threshold analyte concentrationscorrespond to minimum analyte concentrations associated with aparticular renal disorder or disease. At 1714, the particular disorderthat corresponds to the disorder table that has the most disorderthreshold analyte concentrations exceeded by the sample analyteconcentrations is identified as the most likely renal disorder. The oneor more of the analyte concentrations for the sample and the most likelyrenal disorder type is generated for display via the computer at 1716.

It should be appreciated by those of skill in the art that thetechniques disclosed in the examples above represent techniquesdiscovered by the inventors to function well in the practice of theinvention. Those of skill in the art should, however, in light of thepresent disclosure, appreciate that many changes can be made in thespecific embodiments that are disclosed and still obtain a like orsimilar result without departing from the spirit and scope of theinvention, therefore all matter set forth or shown in the accompanyingdrawings is to be interpreted as illustrative and not in a limitingsense.

1. A diagnostic system for determining a renal disorder in a mammal from an assay comprising at least three analyte biomarkers collected from a sample of a body fluid of the mammal, the diagnostic system comprising: a data source comprising a first threshold concentration for each of the at least three biomarker analytes and a second threshold concentration for each of the at least three biomarker analytes for each of a plurality of disorders; a renal disorder determining application comprising modules executable by a processor to determine the renal disorder, the renal disorder determining application comprising: an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three biomarker analytes; a comparison module to: compare each analyte concentration of the sample to a corresponding first threshold concentration; and identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration; an analysis module to: compare each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration associated with each of the at least three biomarker analytes for each of the plurality of disorders; determine a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and identify a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and an output module to generate the one or more analyte concentrations and the particular one of the plurality of disorders for display.
 2. The system of claim 1 wherein the data source comprises: a diagnostic analytic concentrations database to store each of the first threshold concentrations; and a disorder database to store a plurality of tables, each of the plurality tables comprising the second threshold concentration data for each of the at least three biomarker analytes and corresponding to one of the plurality of disorders
 3. The system of claim 2 wherein the plurality of disorders comprises at least one member from a group consisting of glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, acute renal failure, chronic renal failure, nephrosis, nephropathy, polycystic kidney disease, Bright's disease, renal transplant, chronic unilateral obstructive uropathy, chronic bilateral obstructive uropathy, acute unilateral obstructive uropathy, acute bilateral obstructive uropathy, renal damage secondary to a disease state including diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, and Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate diseases, and renal damage caused by exposure to secondary agents and conditions including therapeutic drugs, recreational drugs, contrast agents, toxins, nephrolithiasis, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
 4. The system of claim 1 wherein input device comprises a multiplexed immunoassay device.
 5. The system of claim 4 wherein the multiplexed immunoassay device comprises at least one member of another group consisting of a multiplexed sandwich immunoassay device, a microsphere-based capture-sandwich immunoassay device, and a vibrational detection-based immunoassay device.
 6. The system of claim 1 wherein input device comprises a computer.
 7. The system of claim 1 wherein first threshold concentration corresponds to a maximum concentration of an analyte concentration range associated with a normal renal function.
 8. The system of claim 1 wherein: the data source further comprises a combination threshold concentration for each of a plurality of combinations of concentration of the sixteen biomarker analytes associated with each of the plurality of disorders; and the analysis module is further configured to: assign a weight to each of the one or more analyte concentrations identified by the comparison module; calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the particular renal disorder.
 9. The system of claim 1 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analytes comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 10. The system of claim 1 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analytes comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
 11. A diagnostic system for determining a renal disorder in a mammal from an assay comprising at least three analyte biomarkers collected from a sample of a body fluid of the mammal, the diagnostic system comprising: a renal disorder determining application comprising modules executable by a processor to determine the renal disorder, the renal disorder determining application comprising: an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three analyte biomarkers; a comparison module to: compare each analyte concentration of the sample to a corresponding threshold concentration retrieved from an analyte concentration database; and identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration; an analysis module to: retrieve a combination threshold concentration for each of a plurality of combinations of concentrations of the at least three analyte biomarkers associated with each of a plurality of disorders; and assign a weight to each of the one or more analyte concentrations of the sample identified by the comparison module; calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder; and an output module to generate the one or more analyte concentrations and the renal disorder for display.
 12. The system of claim 11, wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of three analyte types.
 13. The system of claim 11 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 14. The system of claim 11 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
 15. The system of claim 11 wherein input device comprises a multiplexed immunoassay device, and wherein the multiplexed immunoassay device comprises at least one member of another group consisting of a multiplexed sandwich immunoassay device, a microsphere-based capture-sandwich immunoassay device, and a vibrational detection-based immunoassay device.
 16. A computer-readable medium encoded with a renal disorder determining application comprising modules executable by a processor to determine a renal disorder from an assay comprising at least three biomarker analytes collected from a sample of a body fluid of a mammal, the renal disorder determining application comprising: an analyte input module to receive analyte concentrations data for the sample from an input device, the analyte concentrations data comprising analyte concentrations for each of the at least three biomarker analytes; a comparison module to: compare each analyte concentration of the sample to a corresponding first threshold concentration retrieved from a data source; and identify one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration; an analysis module to: compare each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration retrieved from the data source, the corresponding second threshold concentration associated with each of the at least three biomarker analytes for each of a plurality of disorders; determine a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and identify a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and an output module to generate the one or more analyte concentrations and the particular one of the plurality of disorders for display.
 17. The computer-readable medium of claim 16 wherein the data source comprises: a diagnostic analytic concentrations database to store each of the first threshold concentrations; and a disorder database to store a plurality of tables, each of the plurality tables comprising the second threshold concentration data for each of the sixteen biomarker analytes and corresponding to one of the plurality of disorders
 18. The computer-readable medium of claim 16 wherein the plurality of disorders is selected from a group consisting of glomerulonephritis, interstitial nephritis, tubular damage, vasculitis, glomerulosclerosis, acute renal failure, chronic renal failure, nephrosis, nephropathy, polycystic kidney disease, Bright's disease, renal transplant, chronic unilateral obstructive uropathy, chronic bilateral obstructive uropathy, acute unilateral obstructive uropathy, acute bilateral obstructive uropathy, renal damage secondary to a disease state including diabetes, hypertension, autoimmune diseases including lupus, Wegener's granulomatosis, and Goodpasture syndrome, primary hyperoxaluria, kidney transplant rejection, sepsis, nephritis secondary to infection of the kidney, rhabdomyolysis, multiple myeloma, and prostate diseases, and renal damage caused by exposure to secondary agents and conditions including therapeutic drugs, recreational drugs, contrast agents, toxins, nephrolithiasis, ischemia, liver transplantation, heart transplantation, lung transplantation, and hypovolemia.
 19. The computer-readable medium of claim 16 wherein first threshold concentration corresponds to a maximum concentration of an analyte concentration range associated with a normal renal function.
 20. The computer-readable medium of claim 16 wherein: the data source further comprises a combination threshold concentration for each of a plurality of combinations of concentration of the at least three biomarker analytes associated with each of the plurality of disorders; and the analysis module is further configured to: assign a weight to each of the one or more analyte concentrations identified by the comparison module; calculate a combined sample analyte concentration based on the weight assigned to each analyte concentration; and compare a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder to display.
 21. The computer-readable medium of claim 20, wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of three analyte types.
 22. The system of claim 16 wherein the assay comprises at least six analyte biomarkers collected from the sample, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 23. The system of claim 16 wherein the assay comprises at least sixteen analyte biomarkers collected from the sample, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
 24. A method for determining a renal disorder in a mammal, the method comprising: receiving analyte concentrations data for a sample a body fluid of the mammal from an input device, the analyte concentrations data comprising analyte concentrations for each of at least three analyte biomarkers; processing the analyte concentrations data at a processor, the processing comprising: comparing each analyte concentration of the sample to a corresponding first threshold concentration retrieved from a data source; identifying one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration; comparing each of the one or more analyte concentrations of the sample identified by the comparison module to a corresponding second threshold concentration retrieved from the data source, the corresponding second threshold concentration associated with each of the at least three analyte biomarkers for each of the plurality of disorders; determining a number of corresponding second threshold concentrations exceeded by the one or more analyte concentrations of the sample for each of the plurality of disorders; and identifying a particular one of the plurality of disorders having a maximum number of corresponding second threshold concentrations that are exceeded by the one or more analyte concentrations of the sample as the most likely renal disorder; and generating the one or more analyte concentrations and the particular one of the plurality of disorders for display.
 25. The method of claim 24 further comprising: retrieving, at the processor, a combination threshold concentration for each of a plurality of combinations of concentration of the sixteen biomarker analytes associated with each of a plurality of disorders; and assigning a weight to each of the one or more analyte concentrations identified by the comparison module; calculating a combined sample analyte concentration based on the weight assigned to each analyte concentration; and comparing a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder to display.
 26. The method of claim 25 wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same three analyte types.
 27. The method of claim 24 wherein the analyte concentrations data comprises analyte concentrations for each of at least six analyte biomarkers, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 28. The method of claim 24 wherein the analyte concentrations data comprises analyte concentrations for each of at least sixteen analyte biomarkers, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol.
 29. A method for determining a renal disorder in a mammal, the method comprising: receiving analyte concentrations data for a sample a body fluid of the mammal from an input device, the analyte concentrations data comprising analyte concentrations for each of at least three analyte biomarkers; processing the analyte concentrations data at a processor, the processing comprising: comparing each analyte concentration of the sample to a corresponding threshold concentration retrieved from an analyte concentration database; and identifying one or more analyte concentrations of the sample that exceed the corresponding first threshold concentration; retrieving a combination threshold concentration for each of a plurality of combinations of concentrations of the plurality of biomarker analytes associated with each of a plurality of disorders; and assigning a weight to each of the one or more analyte concentrations of the sample identified by the comparison module; calculating a combined sample analyte concentration based on the weight assigned to each analyte concentration; and comparing a corresponding combination threshold concentration to the combined sample analyte concentration to identify the renal disorder; and generating the one or more analyte concentrations and the renal disorder for display.
 30. The method of claim 29 wherein the combined sample analyte concentration and the corresponding combination threshold concentration correspond to a combination of a same grouping of analyte types.
 31. The method of claim 29 wherein the analyte concentrations data comprises analyte concentrations for each of at least six analyte biomarkers, the at least six analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, cystatin C, KIM-1, THP, and TIMP-1.
 32. The method of claim 29 wherein the analyte concentrations data comprises analyte concentrations for each of at least sixteen analyte biomarkers, the at least sixteen analyte biomarkers comprising alpha-1 microglobulin, beta-2 microglobulin, calbindin, clusterin, CTGF, creatinine, cystatin C, GST-alpha, KIM-1, microalbumin, NGAL, osteopontin, THP, TIMP-1, TFF-3, VEGF, BLC, CD40, IGF BP2, MMP3, peptide YY, stem cell factor, TNF RII, AXL, Eotaxin 3, FABP, FGF basic, myoglobin, resistin, TRAIL R3, endothelin 1, NrCAM, Tenascin C, VCAM1, and cortisol. 